A new approach for rule extraction of expert system based on SVM
Based on the SVM’s excellent generalization performance, a new approach is proposed to extract knowledge rules from Support Vector Clustering (SVC). In this method, the first step is to choose the features of the sample data by using Genetic Algorithm for improving the comprehensibility of the knowl...
Ausführliche Beschreibung
Autor*in: |
Li, Ai [verfasserIn] Chen, Guo [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2013 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Measurement - Amsterdam [u.a.] : Elsevier Science, 1983, 47, Seite 715-723 |
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Übergeordnetes Werk: |
volume:47 ; pages:715-723 |
DOI / URN: |
10.1016/j.measurement.2013.08.028 |
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Katalog-ID: |
ELV001652354 |
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245 | 1 | 0 | |a A new approach for rule extraction of expert system based on SVM |
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520 | |a Based on the SVM’s excellent generalization performance, a new approach is proposed to extract knowledge rules from Support Vector Clustering (SVC). In this method, the first step is to choose the features of the sample data by using Genetic Algorithm for improving the comprehensibility of the knowledge rules. Then the SVC algorithm is adopted to obtain the Clustering Distribution Matrix of the sample data whose features have been chosen. Finally, hyper-rectangle rules are constructed using the Clustering Distribution Matrix. To make the rules more concise, and easier to explain, hyper-rectangle rules are simplified further by using rules combinations, dimension reduction and interval extension. In addition, the SMOTE (Synthetic Minority Over-sampling Technique) algorithm is adopted to resample fault samples in order to solve the serious imbalance problem of samples. The UCI datasets are used to validate the new method proposed in this paper, the results compared with other rules extraction methods show that the new approach is more effective. The new method is used to extract knowledge rules for aero-engine oil monitoring expert system, and the results show that the new method can effectively extract knowledge rules for expert system, and break through the bottleneck in expert system knowledge dynamic acquisition. | ||
650 | 4 | |a Support Vector Clustering | |
650 | 4 | |a Support Vector Machine | |
650 | 4 | |a Rule extraction | |
650 | 4 | |a Knowledge acquisition | |
650 | 4 | |a Expert system | |
650 | 4 | |a Genetic Algorithm | |
650 | 4 | |a Feature selection | |
700 | 1 | |a Chen, Guo |e verfasserin |4 aut | |
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allfields |
10.1016/j.measurement.2013.08.028 doi (DE-627)ELV001652354 (ELSEVIER)S0263-2241(13)00387-4 DE-627 ger DE-627 rda eng 660 DE-600 50.21 bkl Li, Ai verfasserin aut A new approach for rule extraction of expert system based on SVM 2013 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Based on the SVM’s excellent generalization performance, a new approach is proposed to extract knowledge rules from Support Vector Clustering (SVC). In this method, the first step is to choose the features of the sample data by using Genetic Algorithm for improving the comprehensibility of the knowledge rules. Then the SVC algorithm is adopted to obtain the Clustering Distribution Matrix of the sample data whose features have been chosen. Finally, hyper-rectangle rules are constructed using the Clustering Distribution Matrix. To make the rules more concise, and easier to explain, hyper-rectangle rules are simplified further by using rules combinations, dimension reduction and interval extension. In addition, the SMOTE (Synthetic Minority Over-sampling Technique) algorithm is adopted to resample fault samples in order to solve the serious imbalance problem of samples. The UCI datasets are used to validate the new method proposed in this paper, the results compared with other rules extraction methods show that the new approach is more effective. The new method is used to extract knowledge rules for aero-engine oil monitoring expert system, and the results show that the new method can effectively extract knowledge rules for expert system, and break through the bottleneck in expert system knowledge dynamic acquisition. Support Vector Clustering Support Vector Machine Rule extraction Knowledge acquisition Expert system Genetic Algorithm Feature selection Chen, Guo verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 47, Seite 715-723 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:47 pages:715-723 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4393 50.21 Messtechnik AR 47 715-723 |
spelling |
10.1016/j.measurement.2013.08.028 doi (DE-627)ELV001652354 (ELSEVIER)S0263-2241(13)00387-4 DE-627 ger DE-627 rda eng 660 DE-600 50.21 bkl Li, Ai verfasserin aut A new approach for rule extraction of expert system based on SVM 2013 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Based on the SVM’s excellent generalization performance, a new approach is proposed to extract knowledge rules from Support Vector Clustering (SVC). In this method, the first step is to choose the features of the sample data by using Genetic Algorithm for improving the comprehensibility of the knowledge rules. Then the SVC algorithm is adopted to obtain the Clustering Distribution Matrix of the sample data whose features have been chosen. Finally, hyper-rectangle rules are constructed using the Clustering Distribution Matrix. To make the rules more concise, and easier to explain, hyper-rectangle rules are simplified further by using rules combinations, dimension reduction and interval extension. In addition, the SMOTE (Synthetic Minority Over-sampling Technique) algorithm is adopted to resample fault samples in order to solve the serious imbalance problem of samples. The UCI datasets are used to validate the new method proposed in this paper, the results compared with other rules extraction methods show that the new approach is more effective. The new method is used to extract knowledge rules for aero-engine oil monitoring expert system, and the results show that the new method can effectively extract knowledge rules for expert system, and break through the bottleneck in expert system knowledge dynamic acquisition. Support Vector Clustering Support Vector Machine Rule extraction Knowledge acquisition Expert system Genetic Algorithm Feature selection Chen, Guo verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 47, Seite 715-723 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:47 pages:715-723 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4393 50.21 Messtechnik AR 47 715-723 |
allfields_unstemmed |
10.1016/j.measurement.2013.08.028 doi (DE-627)ELV001652354 (ELSEVIER)S0263-2241(13)00387-4 DE-627 ger DE-627 rda eng 660 DE-600 50.21 bkl Li, Ai verfasserin aut A new approach for rule extraction of expert system based on SVM 2013 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Based on the SVM’s excellent generalization performance, a new approach is proposed to extract knowledge rules from Support Vector Clustering (SVC). In this method, the first step is to choose the features of the sample data by using Genetic Algorithm for improving the comprehensibility of the knowledge rules. Then the SVC algorithm is adopted to obtain the Clustering Distribution Matrix of the sample data whose features have been chosen. Finally, hyper-rectangle rules are constructed using the Clustering Distribution Matrix. To make the rules more concise, and easier to explain, hyper-rectangle rules are simplified further by using rules combinations, dimension reduction and interval extension. In addition, the SMOTE (Synthetic Minority Over-sampling Technique) algorithm is adopted to resample fault samples in order to solve the serious imbalance problem of samples. The UCI datasets are used to validate the new method proposed in this paper, the results compared with other rules extraction methods show that the new approach is more effective. The new method is used to extract knowledge rules for aero-engine oil monitoring expert system, and the results show that the new method can effectively extract knowledge rules for expert system, and break through the bottleneck in expert system knowledge dynamic acquisition. Support Vector Clustering Support Vector Machine Rule extraction Knowledge acquisition Expert system Genetic Algorithm Feature selection Chen, Guo verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 47, Seite 715-723 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:47 pages:715-723 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4393 50.21 Messtechnik AR 47 715-723 |
allfieldsGer |
10.1016/j.measurement.2013.08.028 doi (DE-627)ELV001652354 (ELSEVIER)S0263-2241(13)00387-4 DE-627 ger DE-627 rda eng 660 DE-600 50.21 bkl Li, Ai verfasserin aut A new approach for rule extraction of expert system based on SVM 2013 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Based on the SVM’s excellent generalization performance, a new approach is proposed to extract knowledge rules from Support Vector Clustering (SVC). In this method, the first step is to choose the features of the sample data by using Genetic Algorithm for improving the comprehensibility of the knowledge rules. Then the SVC algorithm is adopted to obtain the Clustering Distribution Matrix of the sample data whose features have been chosen. Finally, hyper-rectangle rules are constructed using the Clustering Distribution Matrix. To make the rules more concise, and easier to explain, hyper-rectangle rules are simplified further by using rules combinations, dimension reduction and interval extension. In addition, the SMOTE (Synthetic Minority Over-sampling Technique) algorithm is adopted to resample fault samples in order to solve the serious imbalance problem of samples. The UCI datasets are used to validate the new method proposed in this paper, the results compared with other rules extraction methods show that the new approach is more effective. The new method is used to extract knowledge rules for aero-engine oil monitoring expert system, and the results show that the new method can effectively extract knowledge rules for expert system, and break through the bottleneck in expert system knowledge dynamic acquisition. Support Vector Clustering Support Vector Machine Rule extraction Knowledge acquisition Expert system Genetic Algorithm Feature selection Chen, Guo verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 47, Seite 715-723 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:47 pages:715-723 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4393 50.21 Messtechnik AR 47 715-723 |
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10.1016/j.measurement.2013.08.028 doi (DE-627)ELV001652354 (ELSEVIER)S0263-2241(13)00387-4 DE-627 ger DE-627 rda eng 660 DE-600 50.21 bkl Li, Ai verfasserin aut A new approach for rule extraction of expert system based on SVM 2013 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Based on the SVM’s excellent generalization performance, a new approach is proposed to extract knowledge rules from Support Vector Clustering (SVC). In this method, the first step is to choose the features of the sample data by using Genetic Algorithm for improving the comprehensibility of the knowledge rules. Then the SVC algorithm is adopted to obtain the Clustering Distribution Matrix of the sample data whose features have been chosen. Finally, hyper-rectangle rules are constructed using the Clustering Distribution Matrix. To make the rules more concise, and easier to explain, hyper-rectangle rules are simplified further by using rules combinations, dimension reduction and interval extension. In addition, the SMOTE (Synthetic Minority Over-sampling Technique) algorithm is adopted to resample fault samples in order to solve the serious imbalance problem of samples. The UCI datasets are used to validate the new method proposed in this paper, the results compared with other rules extraction methods show that the new approach is more effective. The new method is used to extract knowledge rules for aero-engine oil monitoring expert system, and the results show that the new method can effectively extract knowledge rules for expert system, and break through the bottleneck in expert system knowledge dynamic acquisition. Support Vector Clustering Support Vector Machine Rule extraction Knowledge acquisition Expert system Genetic Algorithm Feature selection Chen, Guo verfasserin aut Enthalten in Measurement Amsterdam [u.a.] : Elsevier Science, 1983 47, Seite 715-723 Online-Ressource (DE-627)320404927 (DE-600)2000550-7 (DE-576)259484342 nnns volume:47 pages:715-723 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA 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_63 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_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 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_4393 50.21 Messtechnik AR 47 715-723 |
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A new approach for rule extraction of expert system based on SVM |
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A new approach for rule extraction of expert system based on SVM |
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Li, Ai |
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Measurement |
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a new approach for rule extraction of expert system based on svm |
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A new approach for rule extraction of expert system based on SVM |
abstract |
Based on the SVM’s excellent generalization performance, a new approach is proposed to extract knowledge rules from Support Vector Clustering (SVC). In this method, the first step is to choose the features of the sample data by using Genetic Algorithm for improving the comprehensibility of the knowledge rules. Then the SVC algorithm is adopted to obtain the Clustering Distribution Matrix of the sample data whose features have been chosen. Finally, hyper-rectangle rules are constructed using the Clustering Distribution Matrix. To make the rules more concise, and easier to explain, hyper-rectangle rules are simplified further by using rules combinations, dimension reduction and interval extension. In addition, the SMOTE (Synthetic Minority Over-sampling Technique) algorithm is adopted to resample fault samples in order to solve the serious imbalance problem of samples. The UCI datasets are used to validate the new method proposed in this paper, the results compared with other rules extraction methods show that the new approach is more effective. The new method is used to extract knowledge rules for aero-engine oil monitoring expert system, and the results show that the new method can effectively extract knowledge rules for expert system, and break through the bottleneck in expert system knowledge dynamic acquisition. |
abstractGer |
Based on the SVM’s excellent generalization performance, a new approach is proposed to extract knowledge rules from Support Vector Clustering (SVC). In this method, the first step is to choose the features of the sample data by using Genetic Algorithm for improving the comprehensibility of the knowledge rules. Then the SVC algorithm is adopted to obtain the Clustering Distribution Matrix of the sample data whose features have been chosen. Finally, hyper-rectangle rules are constructed using the Clustering Distribution Matrix. To make the rules more concise, and easier to explain, hyper-rectangle rules are simplified further by using rules combinations, dimension reduction and interval extension. In addition, the SMOTE (Synthetic Minority Over-sampling Technique) algorithm is adopted to resample fault samples in order to solve the serious imbalance problem of samples. The UCI datasets are used to validate the new method proposed in this paper, the results compared with other rules extraction methods show that the new approach is more effective. The new method is used to extract knowledge rules for aero-engine oil monitoring expert system, and the results show that the new method can effectively extract knowledge rules for expert system, and break through the bottleneck in expert system knowledge dynamic acquisition. |
abstract_unstemmed |
Based on the SVM’s excellent generalization performance, a new approach is proposed to extract knowledge rules from Support Vector Clustering (SVC). In this method, the first step is to choose the features of the sample data by using Genetic Algorithm for improving the comprehensibility of the knowledge rules. Then the SVC algorithm is adopted to obtain the Clustering Distribution Matrix of the sample data whose features have been chosen. Finally, hyper-rectangle rules are constructed using the Clustering Distribution Matrix. To make the rules more concise, and easier to explain, hyper-rectangle rules are simplified further by using rules combinations, dimension reduction and interval extension. In addition, the SMOTE (Synthetic Minority Over-sampling Technique) algorithm is adopted to resample fault samples in order to solve the serious imbalance problem of samples. The UCI datasets are used to validate the new method proposed in this paper, the results compared with other rules extraction methods show that the new approach is more effective. The new method is used to extract knowledge rules for aero-engine oil monitoring expert system, and the results show that the new method can effectively extract knowledge rules for expert system, and break through the bottleneck in expert system knowledge dynamic acquisition. |
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title_short |
A new approach for rule extraction of expert system based on SVM |
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up_date |
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