A fast belief rule base generation and reduction method for classification problems

The belief rule-based (BRB) system has been one of the most significant rule-based systems for its ability to deal with various kinds of information under uncertainty, and it has shown great potential for classification problems. However, the combinatorial explosion problem hugely limits the applica...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Gao, Fei [verfasserIn] Bi, Wenhao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Belief rule base Belief rule-based system Rule reduction Classification problem

Übergeordnetes Werk:	Enthalten in: International journal of approximate reasoning - Amsterdam [u.a.] : Elsevier Science, 1987, 160
Übergeordnetes Werk:	volume:160

DOI / URN:	10.1016/j.ijar.2023.108964

Katalog-ID:	ELV060914971

Internformat


LEADER	01000caa a22002652 4500
001	ELV060914971
003	DE-627
005	20230927074826.0
007	cr uuu---uuuuu
008	230727s2023 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1016/j.ijar.2023.108964 \|2 doi
035			\|a (DE-627)ELV060914971
035			\|a (ELSEVIER)S0888-613X(23)00095-6
040			\|a DE-627 \|b ger \|c DE-627 \|e rda
041			\|a eng
082	0	4	\|a 510 \|q VZ
084			\|a 54.72 \|2 bkl
100	1		\|a Gao, Fei \|e verfasserin \|0 (orcid)0000-0002-9273-4559 \|4 aut
245	1	0	\|a A fast belief rule base generation and reduction method for classification problems
264		1	\|c 2023
336			\|a nicht spezifiziert \|b zzz \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a The belief rule-based (BRB) system has been one of the most significant rule-based systems for its ability to deal with various kinds of information under uncertainty, and it has shown great potential for classification problems. However, the combinatorial explosion problem hugely limits the application of the BRB system as excessive rules could not only increase the computation cost, but also impact the performance of the BRB system. Therefore, motivated by this problem, this paper proposed a fast and accurate belief rule base generation and reduction method. Firstly, a fast belief rule base generation method is introduced, where similar rules are grouped and combined to ensure all the possible situations are covered without generating excessive rules. Then, a redundancy-based belief rule base reduction method is proposed, where the redundancy degree of the belief rule that represents the degree to which a belief rule is affected by other rules is introduced, and it is calculated to identify redundant rules. Furthermore, the conventional evidential reasoning (ER)-based inference process is retained from conventional BRB systems. Thirty classification benchmarks from the well-known UCI machine learning repository are tested to validate the effectiveness of the proposed method, and the results are compared with other rule-based systems, improved BRB systems, and other machine learning methods. Comparison results show that the proposed method could effectively reduce the size of the BRB without costing its accuracy. Furthermore, sensitivity analysis and robustness analysis are conducted, which further show the effectiveness and robustness of the proposed method.
650		4	\|a Belief rule base
650		4	\|a Belief rule-based system
650		4	\|a Rule reduction
650		4	\|a Classification problem
700	1		\|a Bi, Wenhao \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t International journal of approximate reasoning \|d Amsterdam [u.a.] : Elsevier Science, 1987 \|g 160 \|h Online-Ressource \|w (DE-627)320416763 \|w (DE-600)2002042-9 \|w (DE-576)114818037 \|x 0888-613x \|7 nnns
773	1	8	\|g volume:160
912			\|a GBV_USEFLAG_U
912			\|a GBV_ELV
912			\|a SYSFLAG_U
912			\|a SSG-OPC-MAT
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
936	b	k	\|a 54.72 \|j Künstliche Intelligenz \|q VZ
951			\|a AR
952			\|d 160

Indexfelder

author_variant	f g fg w b wb
matchkey_str	article:0888613x:2023----::fsblerlbsgnrtoadeutomtofrls
hierarchy_sort_str	2023
bklnumber	54.72
publishDate	2023
allfields	10.1016/j.ijar.2023.108964 doi (DE-627)ELV060914971 (ELSEVIER)S0888-613X(23)00095-6 DE-627 ger DE-627 rda eng 510 VZ 54.72 bkl Gao, Fei verfasserin (orcid)0000-0002-9273-4559 aut A fast belief rule base generation and reduction method for classification problems 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The belief rule-based (BRB) system has been one of the most significant rule-based systems for its ability to deal with various kinds of information under uncertainty, and it has shown great potential for classification problems. However, the combinatorial explosion problem hugely limits the application of the BRB system as excessive rules could not only increase the computation cost, but also impact the performance of the BRB system. Therefore, motivated by this problem, this paper proposed a fast and accurate belief rule base generation and reduction method. Firstly, a fast belief rule base generation method is introduced, where similar rules are grouped and combined to ensure all the possible situations are covered without generating excessive rules. Then, a redundancy-based belief rule base reduction method is proposed, where the redundancy degree of the belief rule that represents the degree to which a belief rule is affected by other rules is introduced, and it is calculated to identify redundant rules. Furthermore, the conventional evidential reasoning (ER)-based inference process is retained from conventional BRB systems. Thirty classification benchmarks from the well-known UCI machine learning repository are tested to validate the effectiveness of the proposed method, and the results are compared with other rule-based systems, improved BRB systems, and other machine learning methods. Comparison results show that the proposed method could effectively reduce the size of the BRB without costing its accuracy. Furthermore, sensitivity analysis and robustness analysis are conducted, which further show the effectiveness and robustness of the proposed method. Belief rule base Belief rule-based system Rule reduction Classification problem Bi, Wenhao verfasserin aut Enthalten in International journal of approximate reasoning Amsterdam [u.a.] : Elsevier Science, 1987 160 Online-Ressource (DE-627)320416763 (DE-600)2002042-9 (DE-576)114818037 0888-613x nnns volume:160 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 160
spelling	10.1016/j.ijar.2023.108964 doi (DE-627)ELV060914971 (ELSEVIER)S0888-613X(23)00095-6 DE-627 ger DE-627 rda eng 510 VZ 54.72 bkl Gao, Fei verfasserin (orcid)0000-0002-9273-4559 aut A fast belief rule base generation and reduction method for classification problems 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The belief rule-based (BRB) system has been one of the most significant rule-based systems for its ability to deal with various kinds of information under uncertainty, and it has shown great potential for classification problems. However, the combinatorial explosion problem hugely limits the application of the BRB system as excessive rules could not only increase the computation cost, but also impact the performance of the BRB system. Therefore, motivated by this problem, this paper proposed a fast and accurate belief rule base generation and reduction method. Firstly, a fast belief rule base generation method is introduced, where similar rules are grouped and combined to ensure all the possible situations are covered without generating excessive rules. Then, a redundancy-based belief rule base reduction method is proposed, where the redundancy degree of the belief rule that represents the degree to which a belief rule is affected by other rules is introduced, and it is calculated to identify redundant rules. Furthermore, the conventional evidential reasoning (ER)-based inference process is retained from conventional BRB systems. Thirty classification benchmarks from the well-known UCI machine learning repository are tested to validate the effectiveness of the proposed method, and the results are compared with other rule-based systems, improved BRB systems, and other machine learning methods. Comparison results show that the proposed method could effectively reduce the size of the BRB without costing its accuracy. Furthermore, sensitivity analysis and robustness analysis are conducted, which further show the effectiveness and robustness of the proposed method. Belief rule base Belief rule-based system Rule reduction Classification problem Bi, Wenhao verfasserin aut Enthalten in International journal of approximate reasoning Amsterdam [u.a.] : Elsevier Science, 1987 160 Online-Ressource (DE-627)320416763 (DE-600)2002042-9 (DE-576)114818037 0888-613x nnns volume:160 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 160
allfields_unstemmed	10.1016/j.ijar.2023.108964 doi (DE-627)ELV060914971 (ELSEVIER)S0888-613X(23)00095-6 DE-627 ger DE-627 rda eng 510 VZ 54.72 bkl Gao, Fei verfasserin (orcid)0000-0002-9273-4559 aut A fast belief rule base generation and reduction method for classification problems 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The belief rule-based (BRB) system has been one of the most significant rule-based systems for its ability to deal with various kinds of information under uncertainty, and it has shown great potential for classification problems. However, the combinatorial explosion problem hugely limits the application of the BRB system as excessive rules could not only increase the computation cost, but also impact the performance of the BRB system. Therefore, motivated by this problem, this paper proposed a fast and accurate belief rule base generation and reduction method. Firstly, a fast belief rule base generation method is introduced, where similar rules are grouped and combined to ensure all the possible situations are covered without generating excessive rules. Then, a redundancy-based belief rule base reduction method is proposed, where the redundancy degree of the belief rule that represents the degree to which a belief rule is affected by other rules is introduced, and it is calculated to identify redundant rules. Furthermore, the conventional evidential reasoning (ER)-based inference process is retained from conventional BRB systems. Thirty classification benchmarks from the well-known UCI machine learning repository are tested to validate the effectiveness of the proposed method, and the results are compared with other rule-based systems, improved BRB systems, and other machine learning methods. Comparison results show that the proposed method could effectively reduce the size of the BRB without costing its accuracy. Furthermore, sensitivity analysis and robustness analysis are conducted, which further show the effectiveness and robustness of the proposed method. Belief rule base Belief rule-based system Rule reduction Classification problem Bi, Wenhao verfasserin aut Enthalten in International journal of approximate reasoning Amsterdam [u.a.] : Elsevier Science, 1987 160 Online-Ressource (DE-627)320416763 (DE-600)2002042-9 (DE-576)114818037 0888-613x nnns volume:160 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 160
allfieldsGer	10.1016/j.ijar.2023.108964 doi (DE-627)ELV060914971 (ELSEVIER)S0888-613X(23)00095-6 DE-627 ger DE-627 rda eng 510 VZ 54.72 bkl Gao, Fei verfasserin (orcid)0000-0002-9273-4559 aut A fast belief rule base generation and reduction method for classification problems 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The belief rule-based (BRB) system has been one of the most significant rule-based systems for its ability to deal with various kinds of information under uncertainty, and it has shown great potential for classification problems. However, the combinatorial explosion problem hugely limits the application of the BRB system as excessive rules could not only increase the computation cost, but also impact the performance of the BRB system. Therefore, motivated by this problem, this paper proposed a fast and accurate belief rule base generation and reduction method. Firstly, a fast belief rule base generation method is introduced, where similar rules are grouped and combined to ensure all the possible situations are covered without generating excessive rules. Then, a redundancy-based belief rule base reduction method is proposed, where the redundancy degree of the belief rule that represents the degree to which a belief rule is affected by other rules is introduced, and it is calculated to identify redundant rules. Furthermore, the conventional evidential reasoning (ER)-based inference process is retained from conventional BRB systems. Thirty classification benchmarks from the well-known UCI machine learning repository are tested to validate the effectiveness of the proposed method, and the results are compared with other rule-based systems, improved BRB systems, and other machine learning methods. Comparison results show that the proposed method could effectively reduce the size of the BRB without costing its accuracy. Furthermore, sensitivity analysis and robustness analysis are conducted, which further show the effectiveness and robustness of the proposed method. Belief rule base Belief rule-based system Rule reduction Classification problem Bi, Wenhao verfasserin aut Enthalten in International journal of approximate reasoning Amsterdam [u.a.] : Elsevier Science, 1987 160 Online-Ressource (DE-627)320416763 (DE-600)2002042-9 (DE-576)114818037 0888-613x nnns volume:160 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 160
allfieldsSound	10.1016/j.ijar.2023.108964 doi (DE-627)ELV060914971 (ELSEVIER)S0888-613X(23)00095-6 DE-627 ger DE-627 rda eng 510 VZ 54.72 bkl Gao, Fei verfasserin (orcid)0000-0002-9273-4559 aut A fast belief rule base generation and reduction method for classification problems 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The belief rule-based (BRB) system has been one of the most significant rule-based systems for its ability to deal with various kinds of information under uncertainty, and it has shown great potential for classification problems. However, the combinatorial explosion problem hugely limits the application of the BRB system as excessive rules could not only increase the computation cost, but also impact the performance of the BRB system. Therefore, motivated by this problem, this paper proposed a fast and accurate belief rule base generation and reduction method. Firstly, a fast belief rule base generation method is introduced, where similar rules are grouped and combined to ensure all the possible situations are covered without generating excessive rules. Then, a redundancy-based belief rule base reduction method is proposed, where the redundancy degree of the belief rule that represents the degree to which a belief rule is affected by other rules is introduced, and it is calculated to identify redundant rules. Furthermore, the conventional evidential reasoning (ER)-based inference process is retained from conventional BRB systems. Thirty classification benchmarks from the well-known UCI machine learning repository are tested to validate the effectiveness of the proposed method, and the results are compared with other rule-based systems, improved BRB systems, and other machine learning methods. Comparison results show that the proposed method could effectively reduce the size of the BRB without costing its accuracy. Furthermore, sensitivity analysis and robustness analysis are conducted, which further show the effectiveness and robustness of the proposed method. Belief rule base Belief rule-based system Rule reduction Classification problem Bi, Wenhao verfasserin aut Enthalten in International journal of approximate reasoning Amsterdam [u.a.] : Elsevier Science, 1987 160 Online-Ressource (DE-627)320416763 (DE-600)2002042-9 (DE-576)114818037 0888-613x nnns volume:160 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 160
language	English
source	Enthalten in International journal of approximate reasoning 160 volume:160
sourceStr	Enthalten in International journal of approximate reasoning 160 volume:160
format_phy_str_mv	Article
bklname	Künstliche Intelligenz
institution	findex.gbv.de
topic_facet	Belief rule base Belief rule-based system Rule reduction Classification problem
dewey-raw	510
isfreeaccess_bool	false
container_title	International journal of approximate reasoning
authorswithroles_txt_mv	Gao, Fei @@aut@@ Bi, Wenhao @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320416763
dewey-sort	3510
id	ELV060914971
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV060914971</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230927074826.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230727s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.ijar.2023.108964</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV060914971</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0888-613X(23)00095-6</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.72</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Gao, Fei</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-9273-4559</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A fast belief rule base generation and reduction method for classification problems</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The belief rule-based (BRB) system has been one of the most significant rule-based systems for its ability to deal with various kinds of information under uncertainty, and it has shown great potential for classification problems. However, the combinatorial explosion problem hugely limits the application of the BRB system as excessive rules could not only increase the computation cost, but also impact the performance of the BRB system. Therefore, motivated by this problem, this paper proposed a fast and accurate belief rule base generation and reduction method. Firstly, a fast belief rule base generation method is introduced, where similar rules are grouped and combined to ensure all the possible situations are covered without generating excessive rules. Then, a redundancy-based belief rule base reduction method is proposed, where the redundancy degree of the belief rule that represents the degree to which a belief rule is affected by other rules is introduced, and it is calculated to identify redundant rules. Furthermore, the conventional evidential reasoning (ER)-based inference process is retained from conventional BRB systems. Thirty classification benchmarks from the well-known UCI machine learning repository are tested to validate the effectiveness of the proposed method, and the results are compared with other rule-based systems, improved BRB systems, and other machine learning methods. Comparison results show that the proposed method could effectively reduce the size of the BRB without costing its accuracy. Furthermore, sensitivity analysis and robustness analysis are conducted, which further show the effectiveness and robustness of the proposed method.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Belief rule base</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Belief rule-based system</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Rule reduction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Classification problem</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bi, Wenhao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of approximate reasoning</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier Science, 1987</subfield><subfield code="g">160</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)320416763</subfield><subfield code="w">(DE-600)2002042-9</subfield><subfield code="w">(DE-576)114818037</subfield><subfield code="x">0888-613x</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:160</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.72</subfield><subfield code="j">Künstliche Intelligenz</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">160</subfield></datafield></record></collection>
author	Gao, Fei
spellingShingle	Gao, Fei ddc 510 bkl 54.72 misc Belief rule base misc Belief rule-based system misc Rule reduction misc Classification problem A fast belief rule base generation and reduction method for classification problems
authorStr	Gao, Fei
ppnlink_with_tag_str_mv	@@773@@(DE-627)320416763
format	electronic Article
dewey-ones	510 - Mathematics
delete_txt_mv	keep
author_role	aut aut
collection	elsevier
remote_str	true
illustrated	Not Illustrated
issn	0888-613x
topic_title	510 VZ 54.72 bkl A fast belief rule base generation and reduction method for classification problems Belief rule base Belief rule-based system Rule reduction Classification problem
topic	ddc 510 bkl 54.72 misc Belief rule base misc Belief rule-based system misc Rule reduction misc Classification problem
topic_unstemmed	ddc 510 bkl 54.72 misc Belief rule base misc Belief rule-based system misc Rule reduction misc Classification problem
topic_browse	ddc 510 bkl 54.72 misc Belief rule base misc Belief rule-based system misc Rule reduction misc Classification problem
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	International journal of approximate reasoning
hierarchy_parent_id	320416763
dewey-tens	510 - Mathematics
hierarchy_top_title	International journal of approximate reasoning
isfreeaccess_txt	false
familylinks_str_mv	(DE-627)320416763 (DE-600)2002042-9 (DE-576)114818037
title	A fast belief rule base generation and reduction method for classification problems
ctrlnum	(DE-627)ELV060914971 (ELSEVIER)S0888-613X(23)00095-6
title_full	A fast belief rule base generation and reduction method for classification problems
author_sort	Gao, Fei
journal	International journal of approximate reasoning
journalStr	International journal of approximate reasoning
lang_code	eng
isOA_bool	false
dewey-hundreds	500 - Science
recordtype	marc
publishDateSort	2023
contenttype_str_mv	zzz
author_browse	Gao, Fei Bi, Wenhao
container_volume	160
class	510 VZ 54.72 bkl
format_se	Elektronische Aufsätze
author-letter	Gao, Fei
doi_str_mv	10.1016/j.ijar.2023.108964
normlink	(ORCID)0000-0002-9273-4559
normlink_prefix_str_mv	(orcid)0000-0002-9273-4559
dewey-full	510
author2-role	verfasserin
title_sort	a fast belief rule base generation and reduction method for classification problems
title_auth	A fast belief rule base generation and reduction method for classification problems
abstract	The belief rule-based (BRB) system has been one of the most significant rule-based systems for its ability to deal with various kinds of information under uncertainty, and it has shown great potential for classification problems. However, the combinatorial explosion problem hugely limits the application of the BRB system as excessive rules could not only increase the computation cost, but also impact the performance of the BRB system. Therefore, motivated by this problem, this paper proposed a fast and accurate belief rule base generation and reduction method. Firstly, a fast belief rule base generation method is introduced, where similar rules are grouped and combined to ensure all the possible situations are covered without generating excessive rules. Then, a redundancy-based belief rule base reduction method is proposed, where the redundancy degree of the belief rule that represents the degree to which a belief rule is affected by other rules is introduced, and it is calculated to identify redundant rules. Furthermore, the conventional evidential reasoning (ER)-based inference process is retained from conventional BRB systems. Thirty classification benchmarks from the well-known UCI machine learning repository are tested to validate the effectiveness of the proposed method, and the results are compared with other rule-based systems, improved BRB systems, and other machine learning methods. Comparison results show that the proposed method could effectively reduce the size of the BRB without costing its accuracy. Furthermore, sensitivity analysis and robustness analysis are conducted, which further show the effectiveness and robustness of the proposed method.
abstractGer	The belief rule-based (BRB) system has been one of the most significant rule-based systems for its ability to deal with various kinds of information under uncertainty, and it has shown great potential for classification problems. However, the combinatorial explosion problem hugely limits the application of the BRB system as excessive rules could not only increase the computation cost, but also impact the performance of the BRB system. Therefore, motivated by this problem, this paper proposed a fast and accurate belief rule base generation and reduction method. Firstly, a fast belief rule base generation method is introduced, where similar rules are grouped and combined to ensure all the possible situations are covered without generating excessive rules. Then, a redundancy-based belief rule base reduction method is proposed, where the redundancy degree of the belief rule that represents the degree to which a belief rule is affected by other rules is introduced, and it is calculated to identify redundant rules. Furthermore, the conventional evidential reasoning (ER)-based inference process is retained from conventional BRB systems. Thirty classification benchmarks from the well-known UCI machine learning repository are tested to validate the effectiveness of the proposed method, and the results are compared with other rule-based systems, improved BRB systems, and other machine learning methods. Comparison results show that the proposed method could effectively reduce the size of the BRB without costing its accuracy. Furthermore, sensitivity analysis and robustness analysis are conducted, which further show the effectiveness and robustness of the proposed method.
abstract_unstemmed	The belief rule-based (BRB) system has been one of the most significant rule-based systems for its ability to deal with various kinds of information under uncertainty, and it has shown great potential for classification problems. However, the combinatorial explosion problem hugely limits the application of the BRB system as excessive rules could not only increase the computation cost, but also impact the performance of the BRB system. Therefore, motivated by this problem, this paper proposed a fast and accurate belief rule base generation and reduction method. Firstly, a fast belief rule base generation method is introduced, where similar rules are grouped and combined to ensure all the possible situations are covered without generating excessive rules. Then, a redundancy-based belief rule base reduction method is proposed, where the redundancy degree of the belief rule that represents the degree to which a belief rule is affected by other rules is introduced, and it is calculated to identify redundant rules. Furthermore, the conventional evidential reasoning (ER)-based inference process is retained from conventional BRB systems. Thirty classification benchmarks from the well-known UCI machine learning repository are tested to validate the effectiveness of the proposed method, and the results are compared with other rule-based systems, improved BRB systems, and other machine learning methods. Comparison results show that the proposed method could effectively reduce the size of the BRB without costing its accuracy. Furthermore, sensitivity analysis and robustness analysis are conducted, which further show the effectiveness and robustness of the proposed method.
collection_details	GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700
title_short	A fast belief rule base generation and reduction method for classification problems
remote_bool	true
author2	Bi, Wenhao
author2Str	Bi, Wenhao
ppnlink	320416763
mediatype_str_mv	c
isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1016/j.ijar.2023.108964
up_date	2024-07-06T16:59:33.726Z
_version_	1803849746327011328
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV060914971</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230927074826.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230727s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.ijar.2023.108964</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV060914971</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0888-613X(23)00095-6</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.72</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Gao, Fei</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-9273-4559</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A fast belief rule base generation and reduction method for classification problems</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The belief rule-based (BRB) system has been one of the most significant rule-based systems for its ability to deal with various kinds of information under uncertainty, and it has shown great potential for classification problems. However, the combinatorial explosion problem hugely limits the application of the BRB system as excessive rules could not only increase the computation cost, but also impact the performance of the BRB system. Therefore, motivated by this problem, this paper proposed a fast and accurate belief rule base generation and reduction method. Firstly, a fast belief rule base generation method is introduced, where similar rules are grouped and combined to ensure all the possible situations are covered without generating excessive rules. Then, a redundancy-based belief rule base reduction method is proposed, where the redundancy degree of the belief rule that represents the degree to which a belief rule is affected by other rules is introduced, and it is calculated to identify redundant rules. Furthermore, the conventional evidential reasoning (ER)-based inference process is retained from conventional BRB systems. Thirty classification benchmarks from the well-known UCI machine learning repository are tested to validate the effectiveness of the proposed method, and the results are compared with other rule-based systems, improved BRB systems, and other machine learning methods. Comparison results show that the proposed method could effectively reduce the size of the BRB without costing its accuracy. Furthermore, sensitivity analysis and robustness analysis are conducted, which further show the effectiveness and robustness of the proposed method.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Belief rule base</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Belief rule-based system</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Rule reduction</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Classification problem</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bi, Wenhao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of approximate reasoning</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier Science, 1987</subfield><subfield code="g">160</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)320416763</subfield><subfield code="w">(DE-600)2002042-9</subfield><subfield code="w">(DE-576)114818037</subfield><subfield code="x">0888-613x</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:160</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2008</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2088</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2106</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2232</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2470</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">54.72</subfield><subfield code="j">Künstliche Intelligenz</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">160</subfield></datafield></record></collection>
score	7.401787

Nicht das Richtige dabei?

Schreiben Sie uns!

A fast belief rule base generation and reduction method for classification problems

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?