A greedy belief rule base generation and learning method for classification problem
Among many rule-based systems employed in classification problems, the belief rule-based (BRB) system has been significant for its ability to deal with both quantitative and qualitative information under uncertainty. However, it may face excessive information and low accuracy in some applications du...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Gao, Fei [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: Atomic collapse in graphene quantum dots in a magnetic field - Eren, I. ELSEVIER, 2022, the official journal of the World Federation on Soft Computing (WFSC), Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:98 ; year:2021 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.asoc.2020.106856 |
|---|
| Katalog-ID: |
ELV052510409 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV052510409 | ||
| 003 | DE-627 | ||
| 005 | 20230626033357.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 210910s2021 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.asoc.2020.106856 |2 doi | |
| 028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001245.pica |
| 035 | |a (DE-627)ELV052510409 | ||
| 035 | |a (ELSEVIER)S1568-4946(20)30794-8 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 540 |a 530 |q VZ |
| 084 | |a 33.00 |2 bkl | ||
| 100 | 1 | |a Gao, Fei |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a A greedy belief rule base generation and learning method for classification problem |
| 264 | 1 | |c 2021transfer abstract | |
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a Among many rule-based systems employed in classification problems, the belief rule-based (BRB) system has been significant for its ability to deal with both quantitative and qualitative information under uncertainty. However, it may face excessive information and low accuracy in some applications due to the limitations of the conventional BRB generation method. To this end, a greedy-based BRB learning method is proposed in this paper. Firstly, the BRB is generated by selecting a reduced number of belief rules from a set of candidate rules. Then, the BRB learning process is conducted by exploiting a selection and reduction strategy, which searches and selects the optimal rules from candidate rules as well as removes noise and redundant rules. Moreover, the original procedures of the inference process and class estimation are retained from conventional BRB systems. Thirty standard classification benchmarks are tested to validate the effectiveness and efficiency of the proposed method, and the classification results are compared with existing rule-based systems, novel belief rule-based systems, and conventional machine learning methods. The comparison results show that the proposed method could achieve relatively satisfactory accuracy while having a significantly smaller BRB. Furthermore, the results derived from benchmarks with two or three classes show the superior performance of the proposed method compared with some state-of-the-art classification methods. | ||
| 520 | |a Among many rule-based systems employed in classification problems, the belief rule-based (BRB) system has been significant for its ability to deal with both quantitative and qualitative information under uncertainty. However, it may face excessive information and low accuracy in some applications due to the limitations of the conventional BRB generation method. To this end, a greedy-based BRB learning method is proposed in this paper. Firstly, the BRB is generated by selecting a reduced number of belief rules from a set of candidate rules. Then, the BRB learning process is conducted by exploiting a selection and reduction strategy, which searches and selects the optimal rules from candidate rules as well as removes noise and redundant rules. Moreover, the original procedures of the inference process and class estimation are retained from conventional BRB systems. Thirty standard classification benchmarks are tested to validate the effectiveness and efficiency of the proposed method, and the classification results are compared with existing rule-based systems, novel belief rule-based systems, and conventional machine learning methods. The comparison results show that the proposed method could achieve relatively satisfactory accuracy while having a significantly smaller BRB. Furthermore, the results derived from benchmarks with two or three classes show the superior performance of the proposed method compared with some state-of-the-art classification methods. | ||
| 650 | 7 | |a Classification problem |2 Elsevier | |
| 650 | 7 | |a Rule reduction |2 Elsevier | |
| 650 | 7 | |a Belief rule base |2 Elsevier | |
| 650 | 7 | |a Belief rule-based systems |2 Elsevier | |
| 700 | 1 | |a Zhang, An |4 oth | |
| 700 | 1 | |a Bi, Wenhao |4 oth | |
| 700 | 1 | |a Ma, Junwen |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Eren, I. ELSEVIER |t Atomic collapse in graphene quantum dots in a magnetic field |d 2022 |d the official journal of the World Federation on Soft Computing (WFSC) |g Amsterdam [u.a.] |w (DE-627)ELV007866305 |
| 773 | 1 | 8 | |g volume:98 |g year:2021 |g pages:0 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.asoc.2020.106856 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 936 | b | k | |a 33.00 |j Physik: Allgemeines |q VZ |
| 951 | |a AR | ||
| 952 | |d 98 |j 2021 |h 0 | ||
| author_variant |
f g fg |
|---|---|
| matchkey_str |
gaofeizhanganbiwenhaomajunwen:2021----:gedblerlbsgnrtoaderigehdoca |
| hierarchy_sort_str |
2021transfer abstract |
| bklnumber |
33.00 |
| publishDate |
2021 |
| allfields |
10.1016/j.asoc.2020.106856 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001245.pica (DE-627)ELV052510409 (ELSEVIER)S1568-4946(20)30794-8 DE-627 ger DE-627 rakwb eng 540 530 VZ 33.00 bkl Gao, Fei verfasserin aut A greedy belief rule base generation and learning method for classification problem 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Among many rule-based systems employed in classification problems, the belief rule-based (BRB) system has been significant for its ability to deal with both quantitative and qualitative information under uncertainty. However, it may face excessive information and low accuracy in some applications due to the limitations of the conventional BRB generation method. To this end, a greedy-based BRB learning method is proposed in this paper. Firstly, the BRB is generated by selecting a reduced number of belief rules from a set of candidate rules. Then, the BRB learning process is conducted by exploiting a selection and reduction strategy, which searches and selects the optimal rules from candidate rules as well as removes noise and redundant rules. Moreover, the original procedures of the inference process and class estimation are retained from conventional BRB systems. Thirty standard classification benchmarks are tested to validate the effectiveness and efficiency of the proposed method, and the classification results are compared with existing rule-based systems, novel belief rule-based systems, and conventional machine learning methods. The comparison results show that the proposed method could achieve relatively satisfactory accuracy while having a significantly smaller BRB. Furthermore, the results derived from benchmarks with two or three classes show the superior performance of the proposed method compared with some state-of-the-art classification methods. Among many rule-based systems employed in classification problems, the belief rule-based (BRB) system has been significant for its ability to deal with both quantitative and qualitative information under uncertainty. However, it may face excessive information and low accuracy in some applications due to the limitations of the conventional BRB generation method. To this end, a greedy-based BRB learning method is proposed in this paper. Firstly, the BRB is generated by selecting a reduced number of belief rules from a set of candidate rules. Then, the BRB learning process is conducted by exploiting a selection and reduction strategy, which searches and selects the optimal rules from candidate rules as well as removes noise and redundant rules. Moreover, the original procedures of the inference process and class estimation are retained from conventional BRB systems. Thirty standard classification benchmarks are tested to validate the effectiveness and efficiency of the proposed method, and the classification results are compared with existing rule-based systems, novel belief rule-based systems, and conventional machine learning methods. The comparison results show that the proposed method could achieve relatively satisfactory accuracy while having a significantly smaller BRB. Furthermore, the results derived from benchmarks with two or three classes show the superior performance of the proposed method compared with some state-of-the-art classification methods. Classification problem Elsevier Rule reduction Elsevier Belief rule base Elsevier Belief rule-based systems Elsevier Zhang, An oth Bi, Wenhao oth Ma, Junwen oth Enthalten in Elsevier Science Eren, I. ELSEVIER Atomic collapse in graphene quantum dots in a magnetic field 2022 the official journal of the World Federation on Soft Computing (WFSC) Amsterdam [u.a.] (DE-627)ELV007866305 volume:98 year:2021 pages:0 https://doi.org/10.1016/j.asoc.2020.106856 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 33.00 Physik: Allgemeines VZ AR 98 2021 0 |
| spelling |
10.1016/j.asoc.2020.106856 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001245.pica (DE-627)ELV052510409 (ELSEVIER)S1568-4946(20)30794-8 DE-627 ger DE-627 rakwb eng 540 530 VZ 33.00 bkl Gao, Fei verfasserin aut A greedy belief rule base generation and learning method for classification problem 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Among many rule-based systems employed in classification problems, the belief rule-based (BRB) system has been significant for its ability to deal with both quantitative and qualitative information under uncertainty. However, it may face excessive information and low accuracy in some applications due to the limitations of the conventional BRB generation method. To this end, a greedy-based BRB learning method is proposed in this paper. Firstly, the BRB is generated by selecting a reduced number of belief rules from a set of candidate rules. Then, the BRB learning process is conducted by exploiting a selection and reduction strategy, which searches and selects the optimal rules from candidate rules as well as removes noise and redundant rules. Moreover, the original procedures of the inference process and class estimation are retained from conventional BRB systems. Thirty standard classification benchmarks are tested to validate the effectiveness and efficiency of the proposed method, and the classification results are compared with existing rule-based systems, novel belief rule-based systems, and conventional machine learning methods. The comparison results show that the proposed method could achieve relatively satisfactory accuracy while having a significantly smaller BRB. Furthermore, the results derived from benchmarks with two or three classes show the superior performance of the proposed method compared with some state-of-the-art classification methods. Among many rule-based systems employed in classification problems, the belief rule-based (BRB) system has been significant for its ability to deal with both quantitative and qualitative information under uncertainty. However, it may face excessive information and low accuracy in some applications due to the limitations of the conventional BRB generation method. To this end, a greedy-based BRB learning method is proposed in this paper. Firstly, the BRB is generated by selecting a reduced number of belief rules from a set of candidate rules. Then, the BRB learning process is conducted by exploiting a selection and reduction strategy, which searches and selects the optimal rules from candidate rules as well as removes noise and redundant rules. Moreover, the original procedures of the inference process and class estimation are retained from conventional BRB systems. Thirty standard classification benchmarks are tested to validate the effectiveness and efficiency of the proposed method, and the classification results are compared with existing rule-based systems, novel belief rule-based systems, and conventional machine learning methods. The comparison results show that the proposed method could achieve relatively satisfactory accuracy while having a significantly smaller BRB. Furthermore, the results derived from benchmarks with two or three classes show the superior performance of the proposed method compared with some state-of-the-art classification methods. Classification problem Elsevier Rule reduction Elsevier Belief rule base Elsevier Belief rule-based systems Elsevier Zhang, An oth Bi, Wenhao oth Ma, Junwen oth Enthalten in Elsevier Science Eren, I. ELSEVIER Atomic collapse in graphene quantum dots in a magnetic field 2022 the official journal of the World Federation on Soft Computing (WFSC) Amsterdam [u.a.] (DE-627)ELV007866305 volume:98 year:2021 pages:0 https://doi.org/10.1016/j.asoc.2020.106856 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 33.00 Physik: Allgemeines VZ AR 98 2021 0 |
| allfields_unstemmed |
10.1016/j.asoc.2020.106856 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001245.pica (DE-627)ELV052510409 (ELSEVIER)S1568-4946(20)30794-8 DE-627 ger DE-627 rakwb eng 540 530 VZ 33.00 bkl Gao, Fei verfasserin aut A greedy belief rule base generation and learning method for classification problem 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Among many rule-based systems employed in classification problems, the belief rule-based (BRB) system has been significant for its ability to deal with both quantitative and qualitative information under uncertainty. However, it may face excessive information and low accuracy in some applications due to the limitations of the conventional BRB generation method. To this end, a greedy-based BRB learning method is proposed in this paper. Firstly, the BRB is generated by selecting a reduced number of belief rules from a set of candidate rules. Then, the BRB learning process is conducted by exploiting a selection and reduction strategy, which searches and selects the optimal rules from candidate rules as well as removes noise and redundant rules. Moreover, the original procedures of the inference process and class estimation are retained from conventional BRB systems. Thirty standard classification benchmarks are tested to validate the effectiveness and efficiency of the proposed method, and the classification results are compared with existing rule-based systems, novel belief rule-based systems, and conventional machine learning methods. The comparison results show that the proposed method could achieve relatively satisfactory accuracy while having a significantly smaller BRB. Furthermore, the results derived from benchmarks with two or three classes show the superior performance of the proposed method compared with some state-of-the-art classification methods. Among many rule-based systems employed in classification problems, the belief rule-based (BRB) system has been significant for its ability to deal with both quantitative and qualitative information under uncertainty. However, it may face excessive information and low accuracy in some applications due to the limitations of the conventional BRB generation method. To this end, a greedy-based BRB learning method is proposed in this paper. Firstly, the BRB is generated by selecting a reduced number of belief rules from a set of candidate rules. Then, the BRB learning process is conducted by exploiting a selection and reduction strategy, which searches and selects the optimal rules from candidate rules as well as removes noise and redundant rules. Moreover, the original procedures of the inference process and class estimation are retained from conventional BRB systems. Thirty standard classification benchmarks are tested to validate the effectiveness and efficiency of the proposed method, and the classification results are compared with existing rule-based systems, novel belief rule-based systems, and conventional machine learning methods. The comparison results show that the proposed method could achieve relatively satisfactory accuracy while having a significantly smaller BRB. Furthermore, the results derived from benchmarks with two or three classes show the superior performance of the proposed method compared with some state-of-the-art classification methods. Classification problem Elsevier Rule reduction Elsevier Belief rule base Elsevier Belief rule-based systems Elsevier Zhang, An oth Bi, Wenhao oth Ma, Junwen oth Enthalten in Elsevier Science Eren, I. ELSEVIER Atomic collapse in graphene quantum dots in a magnetic field 2022 the official journal of the World Federation on Soft Computing (WFSC) Amsterdam [u.a.] (DE-627)ELV007866305 volume:98 year:2021 pages:0 https://doi.org/10.1016/j.asoc.2020.106856 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 33.00 Physik: Allgemeines VZ AR 98 2021 0 |
| allfieldsGer |
10.1016/j.asoc.2020.106856 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001245.pica (DE-627)ELV052510409 (ELSEVIER)S1568-4946(20)30794-8 DE-627 ger DE-627 rakwb eng 540 530 VZ 33.00 bkl Gao, Fei verfasserin aut A greedy belief rule base generation and learning method for classification problem 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Among many rule-based systems employed in classification problems, the belief rule-based (BRB) system has been significant for its ability to deal with both quantitative and qualitative information under uncertainty. However, it may face excessive information and low accuracy in some applications due to the limitations of the conventional BRB generation method. To this end, a greedy-based BRB learning method is proposed in this paper. Firstly, the BRB is generated by selecting a reduced number of belief rules from a set of candidate rules. Then, the BRB learning process is conducted by exploiting a selection and reduction strategy, which searches and selects the optimal rules from candidate rules as well as removes noise and redundant rules. Moreover, the original procedures of the inference process and class estimation are retained from conventional BRB systems. Thirty standard classification benchmarks are tested to validate the effectiveness and efficiency of the proposed method, and the classification results are compared with existing rule-based systems, novel belief rule-based systems, and conventional machine learning methods. The comparison results show that the proposed method could achieve relatively satisfactory accuracy while having a significantly smaller BRB. Furthermore, the results derived from benchmarks with two or three classes show the superior performance of the proposed method compared with some state-of-the-art classification methods. Among many rule-based systems employed in classification problems, the belief rule-based (BRB) system has been significant for its ability to deal with both quantitative and qualitative information under uncertainty. However, it may face excessive information and low accuracy in some applications due to the limitations of the conventional BRB generation method. To this end, a greedy-based BRB learning method is proposed in this paper. Firstly, the BRB is generated by selecting a reduced number of belief rules from a set of candidate rules. Then, the BRB learning process is conducted by exploiting a selection and reduction strategy, which searches and selects the optimal rules from candidate rules as well as removes noise and redundant rules. Moreover, the original procedures of the inference process and class estimation are retained from conventional BRB systems. Thirty standard classification benchmarks are tested to validate the effectiveness and efficiency of the proposed method, and the classification results are compared with existing rule-based systems, novel belief rule-based systems, and conventional machine learning methods. The comparison results show that the proposed method could achieve relatively satisfactory accuracy while having a significantly smaller BRB. Furthermore, the results derived from benchmarks with two or three classes show the superior performance of the proposed method compared with some state-of-the-art classification methods. Classification problem Elsevier Rule reduction Elsevier Belief rule base Elsevier Belief rule-based systems Elsevier Zhang, An oth Bi, Wenhao oth Ma, Junwen oth Enthalten in Elsevier Science Eren, I. ELSEVIER Atomic collapse in graphene quantum dots in a magnetic field 2022 the official journal of the World Federation on Soft Computing (WFSC) Amsterdam [u.a.] (DE-627)ELV007866305 volume:98 year:2021 pages:0 https://doi.org/10.1016/j.asoc.2020.106856 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 33.00 Physik: Allgemeines VZ AR 98 2021 0 |
| allfieldsSound |
10.1016/j.asoc.2020.106856 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001245.pica (DE-627)ELV052510409 (ELSEVIER)S1568-4946(20)30794-8 DE-627 ger DE-627 rakwb eng 540 530 VZ 33.00 bkl Gao, Fei verfasserin aut A greedy belief rule base generation and learning method for classification problem 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Among many rule-based systems employed in classification problems, the belief rule-based (BRB) system has been significant for its ability to deal with both quantitative and qualitative information under uncertainty. However, it may face excessive information and low accuracy in some applications due to the limitations of the conventional BRB generation method. To this end, a greedy-based BRB learning method is proposed in this paper. Firstly, the BRB is generated by selecting a reduced number of belief rules from a set of candidate rules. Then, the BRB learning process is conducted by exploiting a selection and reduction strategy, which searches and selects the optimal rules from candidate rules as well as removes noise and redundant rules. Moreover, the original procedures of the inference process and class estimation are retained from conventional BRB systems. Thirty standard classification benchmarks are tested to validate the effectiveness and efficiency of the proposed method, and the classification results are compared with existing rule-based systems, novel belief rule-based systems, and conventional machine learning methods. The comparison results show that the proposed method could achieve relatively satisfactory accuracy while having a significantly smaller BRB. Furthermore, the results derived from benchmarks with two or three classes show the superior performance of the proposed method compared with some state-of-the-art classification methods. Among many rule-based systems employed in classification problems, the belief rule-based (BRB) system has been significant for its ability to deal with both quantitative and qualitative information under uncertainty. However, it may face excessive information and low accuracy in some applications due to the limitations of the conventional BRB generation method. To this end, a greedy-based BRB learning method is proposed in this paper. Firstly, the BRB is generated by selecting a reduced number of belief rules from a set of candidate rules. Then, the BRB learning process is conducted by exploiting a selection and reduction strategy, which searches and selects the optimal rules from candidate rules as well as removes noise and redundant rules. Moreover, the original procedures of the inference process and class estimation are retained from conventional BRB systems. Thirty standard classification benchmarks are tested to validate the effectiveness and efficiency of the proposed method, and the classification results are compared with existing rule-based systems, novel belief rule-based systems, and conventional machine learning methods. The comparison results show that the proposed method could achieve relatively satisfactory accuracy while having a significantly smaller BRB. Furthermore, the results derived from benchmarks with two or three classes show the superior performance of the proposed method compared with some state-of-the-art classification methods. Classification problem Elsevier Rule reduction Elsevier Belief rule base Elsevier Belief rule-based systems Elsevier Zhang, An oth Bi, Wenhao oth Ma, Junwen oth Enthalten in Elsevier Science Eren, I. ELSEVIER Atomic collapse in graphene quantum dots in a magnetic field 2022 the official journal of the World Federation on Soft Computing (WFSC) Amsterdam [u.a.] (DE-627)ELV007866305 volume:98 year:2021 pages:0 https://doi.org/10.1016/j.asoc.2020.106856 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 33.00 Physik: Allgemeines VZ AR 98 2021 0 |
| language |
English |
| source |
Enthalten in Atomic collapse in graphene quantum dots in a magnetic field Amsterdam [u.a.] volume:98 year:2021 pages:0 |
| sourceStr |
Enthalten in Atomic collapse in graphene quantum dots in a magnetic field Amsterdam [u.a.] volume:98 year:2021 pages:0 |
| format_phy_str_mv |
Article |
| bklname |
Physik: Allgemeines |
| institution |
findex.gbv.de |
| topic_facet |
Classification problem Rule reduction Belief rule base Belief rule-based systems |
| dewey-raw |
540 |
| isfreeaccess_bool |
false |
| container_title |
Atomic collapse in graphene quantum dots in a magnetic field |
| authorswithroles_txt_mv |
Gao, Fei @@aut@@ Zhang, An @@oth@@ Bi, Wenhao @@oth@@ Ma, Junwen @@oth@@ |
| publishDateDaySort_date |
2021-01-01T00:00:00Z |
| hierarchy_top_id |
ELV007866305 |
| dewey-sort |
3540 |
| id |
ELV052510409 |
| 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">ELV052510409</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626033357.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210910s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.asoc.2020.106856</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001245.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV052510409</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1568-4946(20)30794-8</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">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">540</subfield><subfield code="a">530</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">33.00</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="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A greedy belief rule base generation and learning method for classification problem</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021transfer abstract</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">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Among many rule-based systems employed in classification problems, the belief rule-based (BRB) system has been significant for its ability to deal with both quantitative and qualitative information under uncertainty. However, it may face excessive information and low accuracy in some applications due to the limitations of the conventional BRB generation method. To this end, a greedy-based BRB learning method is proposed in this paper. Firstly, the BRB is generated by selecting a reduced number of belief rules from a set of candidate rules. Then, the BRB learning process is conducted by exploiting a selection and reduction strategy, which searches and selects the optimal rules from candidate rules as well as removes noise and redundant rules. Moreover, the original procedures of the inference process and class estimation are retained from conventional BRB systems. Thirty standard classification benchmarks are tested to validate the effectiveness and efficiency of the proposed method, and the classification results are compared with existing rule-based systems, novel belief rule-based systems, and conventional machine learning methods. The comparison results show that the proposed method could achieve relatively satisfactory accuracy while having a significantly smaller BRB. Furthermore, the results derived from benchmarks with two or three classes show the superior performance of the proposed method compared with some state-of-the-art classification methods.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Among many rule-based systems employed in classification problems, the belief rule-based (BRB) system has been significant for its ability to deal with both quantitative and qualitative information under uncertainty. However, it may face excessive information and low accuracy in some applications due to the limitations of the conventional BRB generation method. To this end, a greedy-based BRB learning method is proposed in this paper. Firstly, the BRB is generated by selecting a reduced number of belief rules from a set of candidate rules. Then, the BRB learning process is conducted by exploiting a selection and reduction strategy, which searches and selects the optimal rules from candidate rules as well as removes noise and redundant rules. Moreover, the original procedures of the inference process and class estimation are retained from conventional BRB systems. Thirty standard classification benchmarks are tested to validate the effectiveness and efficiency of the proposed method, and the classification results are compared with existing rule-based systems, novel belief rule-based systems, and conventional machine learning methods. The comparison results show that the proposed method could achieve relatively satisfactory accuracy while having a significantly smaller BRB. Furthermore, the results derived from benchmarks with two or three classes show the superior performance of the proposed method compared with some state-of-the-art classification methods.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Classification problem</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Rule reduction</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Belief rule base</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Belief rule-based systems</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, An</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bi, Wenhao</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ma, Junwen</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Eren, I. ELSEVIER</subfield><subfield code="t">Atomic collapse in graphene quantum dots in a magnetic field</subfield><subfield code="d">2022</subfield><subfield code="d">the official journal of the World Federation on Soft Computing (WFSC)</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV007866305</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:98</subfield><subfield code="g">year:2021</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.asoc.2020.106856</subfield><subfield code="3">Volltext</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="936" ind1="b" ind2="k"><subfield code="a">33.00</subfield><subfield code="j">Physik: Allgemeines</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">98</subfield><subfield code="j">2021</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| author |
Gao, Fei |
| spellingShingle |
Gao, Fei ddc 540 bkl 33.00 Elsevier Classification problem Elsevier Rule reduction Elsevier Belief rule base Elsevier Belief rule-based systems A greedy belief rule base generation and learning method for classification problem |
| authorStr |
Gao, Fei |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV007866305 |
| format |
electronic Article |
| dewey-ones |
540 - Chemistry & allied sciences 530 - Physics |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
540 530 VZ 33.00 bkl A greedy belief rule base generation and learning method for classification problem Classification problem Elsevier Rule reduction Elsevier Belief rule base Elsevier Belief rule-based systems Elsevier |
| topic |
ddc 540 bkl 33.00 Elsevier Classification problem Elsevier Rule reduction Elsevier Belief rule base Elsevier Belief rule-based systems |
| topic_unstemmed |
ddc 540 bkl 33.00 Elsevier Classification problem Elsevier Rule reduction Elsevier Belief rule base Elsevier Belief rule-based systems |
| topic_browse |
ddc 540 bkl 33.00 Elsevier Classification problem Elsevier Rule reduction Elsevier Belief rule base Elsevier Belief rule-based systems |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
a z az w b wb j m jm |
| hierarchy_parent_title |
Atomic collapse in graphene quantum dots in a magnetic field |
| hierarchy_parent_id |
ELV007866305 |
| dewey-tens |
540 - Chemistry 530 - Physics |
| hierarchy_top_title |
Atomic collapse in graphene quantum dots in a magnetic field |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV007866305 |
| title |
A greedy belief rule base generation and learning method for classification problem |
| ctrlnum |
(DE-627)ELV052510409 (ELSEVIER)S1568-4946(20)30794-8 |
| title_full |
A greedy belief rule base generation and learning method for classification problem |
| author_sort |
Gao, Fei |
| journal |
Atomic collapse in graphene quantum dots in a magnetic field |
| journalStr |
Atomic collapse in graphene quantum dots in a magnetic field |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
500 - Science |
| recordtype |
marc |
| publishDateSort |
2021 |
| contenttype_str_mv |
zzz |
| container_start_page |
0 |
| author_browse |
Gao, Fei |
| container_volume |
98 |
| class |
540 530 VZ 33.00 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Gao, Fei |
| doi_str_mv |
10.1016/j.asoc.2020.106856 |
| dewey-full |
540 530 |
| title_sort |
a greedy belief rule base generation and learning method for classification problem |
| title_auth |
A greedy belief rule base generation and learning method for classification problem |
| abstract |
Among many rule-based systems employed in classification problems, the belief rule-based (BRB) system has been significant for its ability to deal with both quantitative and qualitative information under uncertainty. However, it may face excessive information and low accuracy in some applications due to the limitations of the conventional BRB generation method. To this end, a greedy-based BRB learning method is proposed in this paper. Firstly, the BRB is generated by selecting a reduced number of belief rules from a set of candidate rules. Then, the BRB learning process is conducted by exploiting a selection and reduction strategy, which searches and selects the optimal rules from candidate rules as well as removes noise and redundant rules. Moreover, the original procedures of the inference process and class estimation are retained from conventional BRB systems. Thirty standard classification benchmarks are tested to validate the effectiveness and efficiency of the proposed method, and the classification results are compared with existing rule-based systems, novel belief rule-based systems, and conventional machine learning methods. The comparison results show that the proposed method could achieve relatively satisfactory accuracy while having a significantly smaller BRB. Furthermore, the results derived from benchmarks with two or three classes show the superior performance of the proposed method compared with some state-of-the-art classification methods. |
| abstractGer |
Among many rule-based systems employed in classification problems, the belief rule-based (BRB) system has been significant for its ability to deal with both quantitative and qualitative information under uncertainty. However, it may face excessive information and low accuracy in some applications due to the limitations of the conventional BRB generation method. To this end, a greedy-based BRB learning method is proposed in this paper. Firstly, the BRB is generated by selecting a reduced number of belief rules from a set of candidate rules. Then, the BRB learning process is conducted by exploiting a selection and reduction strategy, which searches and selects the optimal rules from candidate rules as well as removes noise and redundant rules. Moreover, the original procedures of the inference process and class estimation are retained from conventional BRB systems. Thirty standard classification benchmarks are tested to validate the effectiveness and efficiency of the proposed method, and the classification results are compared with existing rule-based systems, novel belief rule-based systems, and conventional machine learning methods. The comparison results show that the proposed method could achieve relatively satisfactory accuracy while having a significantly smaller BRB. Furthermore, the results derived from benchmarks with two or three classes show the superior performance of the proposed method compared with some state-of-the-art classification methods. |
| abstract_unstemmed |
Among many rule-based systems employed in classification problems, the belief rule-based (BRB) system has been significant for its ability to deal with both quantitative and qualitative information under uncertainty. However, it may face excessive information and low accuracy in some applications due to the limitations of the conventional BRB generation method. To this end, a greedy-based BRB learning method is proposed in this paper. Firstly, the BRB is generated by selecting a reduced number of belief rules from a set of candidate rules. Then, the BRB learning process is conducted by exploiting a selection and reduction strategy, which searches and selects the optimal rules from candidate rules as well as removes noise and redundant rules. Moreover, the original procedures of the inference process and class estimation are retained from conventional BRB systems. Thirty standard classification benchmarks are tested to validate the effectiveness and efficiency of the proposed method, and the classification results are compared with existing rule-based systems, novel belief rule-based systems, and conventional machine learning methods. The comparison results show that the proposed method could achieve relatively satisfactory accuracy while having a significantly smaller BRB. Furthermore, the results derived from benchmarks with two or three classes show the superior performance of the proposed method compared with some state-of-the-art classification methods. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U |
| title_short |
A greedy belief rule base generation and learning method for classification problem |
| url |
https://doi.org/10.1016/j.asoc.2020.106856 |
| remote_bool |
true |
| author2 |
Zhang, An Bi, Wenhao Ma, Junwen |
| author2Str |
Zhang, An Bi, Wenhao Ma, Junwen |
| ppnlink |
ELV007866305 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth |
| doi_str |
10.1016/j.asoc.2020.106856 |
| up_date |
2024-07-06T23:13:46.131Z |
| _version_ |
1803873289378988032 |
| 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">ELV052510409</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626033357.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210910s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.asoc.2020.106856</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001245.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV052510409</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1568-4946(20)30794-8</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">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">540</subfield><subfield code="a">530</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">33.00</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="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A greedy belief rule base generation and learning method for classification problem</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021transfer abstract</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">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Among many rule-based systems employed in classification problems, the belief rule-based (BRB) system has been significant for its ability to deal with both quantitative and qualitative information under uncertainty. However, it may face excessive information and low accuracy in some applications due to the limitations of the conventional BRB generation method. To this end, a greedy-based BRB learning method is proposed in this paper. Firstly, the BRB is generated by selecting a reduced number of belief rules from a set of candidate rules. Then, the BRB learning process is conducted by exploiting a selection and reduction strategy, which searches and selects the optimal rules from candidate rules as well as removes noise and redundant rules. Moreover, the original procedures of the inference process and class estimation are retained from conventional BRB systems. Thirty standard classification benchmarks are tested to validate the effectiveness and efficiency of the proposed method, and the classification results are compared with existing rule-based systems, novel belief rule-based systems, and conventional machine learning methods. The comparison results show that the proposed method could achieve relatively satisfactory accuracy while having a significantly smaller BRB. Furthermore, the results derived from benchmarks with two or three classes show the superior performance of the proposed method compared with some state-of-the-art classification methods.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Among many rule-based systems employed in classification problems, the belief rule-based (BRB) system has been significant for its ability to deal with both quantitative and qualitative information under uncertainty. However, it may face excessive information and low accuracy in some applications due to the limitations of the conventional BRB generation method. To this end, a greedy-based BRB learning method is proposed in this paper. Firstly, the BRB is generated by selecting a reduced number of belief rules from a set of candidate rules. Then, the BRB learning process is conducted by exploiting a selection and reduction strategy, which searches and selects the optimal rules from candidate rules as well as removes noise and redundant rules. Moreover, the original procedures of the inference process and class estimation are retained from conventional BRB systems. Thirty standard classification benchmarks are tested to validate the effectiveness and efficiency of the proposed method, and the classification results are compared with existing rule-based systems, novel belief rule-based systems, and conventional machine learning methods. The comparison results show that the proposed method could achieve relatively satisfactory accuracy while having a significantly smaller BRB. Furthermore, the results derived from benchmarks with two or three classes show the superior performance of the proposed method compared with some state-of-the-art classification methods.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Classification problem</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Rule reduction</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Belief rule base</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Belief rule-based systems</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, An</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bi, Wenhao</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ma, Junwen</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Eren, I. ELSEVIER</subfield><subfield code="t">Atomic collapse in graphene quantum dots in a magnetic field</subfield><subfield code="d">2022</subfield><subfield code="d">the official journal of the World Federation on Soft Computing (WFSC)</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV007866305</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:98</subfield><subfield code="g">year:2021</subfield><subfield code="g">pages:0</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.asoc.2020.106856</subfield><subfield code="3">Volltext</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="936" ind1="b" ind2="k"><subfield code="a">33.00</subfield><subfield code="j">Physik: Allgemeines</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">98</subfield><subfield code="j">2021</subfield><subfield code="h">0</subfield></datafield></record></collection>
|
| score |
7.401947 |