GA-SVM based feature selection and parameter optimization in hospitalization expense modeling
Feature selection and parameter optimization are two important aspects to improve the performance of classifier. A novel approach based on the genetic algorithm(GA) for feature selection and parameter optimization of support vector machine(SVM) is proposed in order to improve the prediction accuracy...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Tao, Zhou [verfasserIn] Huiling, Lu [verfasserIn] Wenwen, Wang [verfasserIn] Xia, Yong [verfasserIn] |
|---|
| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Applied soft computing - Amsterdam [u.a.] : Elsevier Science, 2001, 75, Seite 323-332 |
|---|---|
| Übergeordnetes Werk: |
volume:75 ; pages:323-332 |
| DOI / URN: |
10.1016/j.asoc.2018.11.001 |
|---|
| Katalog-ID: |
ELV001353381 |
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| 245 | 1 | 0 | |a GA-SVM based feature selection and parameter optimization in hospitalization expense modeling |
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| 520 | |a Feature selection and parameter optimization are two important aspects to improve the performance of classifier. A novel approach based on the genetic algorithm(GA) for feature selection and parameter optimization of support vector machine(SVM) is proposed in order to improve the prediction accuracy of hospitalization expense model. First of all, the data of hospitalization expense are preprocessed, including data cleaning, discretization, normalization; Secondly, using k-means to cluster and obtain two category labels; Thirdly, kernel penalty factor c, kernel function γ and the feature mask are used to construct chromosome; The Fourth, a weighted combination of classification accuracy and feature number are taken as the fitness function, and GA was used to optimize the SVM parameters, and simultaneously select the optimal subset of features; Finally, single parameter optimization is performed using GA and particle swarm optimization (PSO), and the optimization performance of which is compared with that of GA-PCA and PSO-PCA. Experimental results show that the proposed algorithm can be used to quickly obtain suitable feature subsets and SVM parameters, thereby achieving a better classification result. | ||
| 650 | 4 | |a Hospitalization expense | |
| 650 | 4 | |a Genetic algorithm | |
| 650 | 4 | |a SVM | |
| 650 | 4 | |a Feature selection | |
| 650 | 4 | |a Parameter optimization | |
| 700 | 1 | |a Huiling, Lu |e verfasserin |4 aut | |
| 700 | 1 | |a Wenwen, Wang |e verfasserin |4 aut | |
| 700 | 1 | |a Xia, Yong |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Applied soft computing |d Amsterdam [u.a.] : Elsevier Science, 2001 |g 75, Seite 323-332 |h Online-Ressource |w (DE-627)334375754 |w (DE-600)2057709-6 |w (DE-576)256145733 |x 1568-4946 |7 nnns |
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10.1016/j.asoc.2018.11.001 doi (DE-627)ELV001353381 (ELSEVIER)S1568-4946(18)30626-4 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Tao, Zhou verfasserin (orcid)0000-0002-8145-712X aut GA-SVM based feature selection and parameter optimization in hospitalization expense modeling 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Feature selection and parameter optimization are two important aspects to improve the performance of classifier. A novel approach based on the genetic algorithm(GA) for feature selection and parameter optimization of support vector machine(SVM) is proposed in order to improve the prediction accuracy of hospitalization expense model. First of all, the data of hospitalization expense are preprocessed, including data cleaning, discretization, normalization; Secondly, using k-means to cluster and obtain two category labels; Thirdly, kernel penalty factor c, kernel function γ and the feature mask are used to construct chromosome; The Fourth, a weighted combination of classification accuracy and feature number are taken as the fitness function, and GA was used to optimize the SVM parameters, and simultaneously select the optimal subset of features; Finally, single parameter optimization is performed using GA and particle swarm optimization (PSO), and the optimization performance of which is compared with that of GA-PCA and PSO-PCA. Experimental results show that the proposed algorithm can be used to quickly obtain suitable feature subsets and SVM parameters, thereby achieving a better classification result. Hospitalization expense Genetic algorithm SVM Feature selection Parameter optimization Huiling, Lu verfasserin aut Wenwen, Wang verfasserin aut Xia, Yong verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 75, Seite 323-332 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:75 pages:323-332 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 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_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_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 54.00 Informatik: Allgemeines AR 75 323-332 |
| spelling |
10.1016/j.asoc.2018.11.001 doi (DE-627)ELV001353381 (ELSEVIER)S1568-4946(18)30626-4 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Tao, Zhou verfasserin (orcid)0000-0002-8145-712X aut GA-SVM based feature selection and parameter optimization in hospitalization expense modeling 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Feature selection and parameter optimization are two important aspects to improve the performance of classifier. A novel approach based on the genetic algorithm(GA) for feature selection and parameter optimization of support vector machine(SVM) is proposed in order to improve the prediction accuracy of hospitalization expense model. First of all, the data of hospitalization expense are preprocessed, including data cleaning, discretization, normalization; Secondly, using k-means to cluster and obtain two category labels; Thirdly, kernel penalty factor c, kernel function γ and the feature mask are used to construct chromosome; The Fourth, a weighted combination of classification accuracy and feature number are taken as the fitness function, and GA was used to optimize the SVM parameters, and simultaneously select the optimal subset of features; Finally, single parameter optimization is performed using GA and particle swarm optimization (PSO), and the optimization performance of which is compared with that of GA-PCA and PSO-PCA. Experimental results show that the proposed algorithm can be used to quickly obtain suitable feature subsets and SVM parameters, thereby achieving a better classification result. Hospitalization expense Genetic algorithm SVM Feature selection Parameter optimization Huiling, Lu verfasserin aut Wenwen, Wang verfasserin aut Xia, Yong verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 75, Seite 323-332 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:75 pages:323-332 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 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_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_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 54.00 Informatik: Allgemeines AR 75 323-332 |
| allfields_unstemmed |
10.1016/j.asoc.2018.11.001 doi (DE-627)ELV001353381 (ELSEVIER)S1568-4946(18)30626-4 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Tao, Zhou verfasserin (orcid)0000-0002-8145-712X aut GA-SVM based feature selection and parameter optimization in hospitalization expense modeling 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Feature selection and parameter optimization are two important aspects to improve the performance of classifier. A novel approach based on the genetic algorithm(GA) for feature selection and parameter optimization of support vector machine(SVM) is proposed in order to improve the prediction accuracy of hospitalization expense model. First of all, the data of hospitalization expense are preprocessed, including data cleaning, discretization, normalization; Secondly, using k-means to cluster and obtain two category labels; Thirdly, kernel penalty factor c, kernel function γ and the feature mask are used to construct chromosome; The Fourth, a weighted combination of classification accuracy and feature number are taken as the fitness function, and GA was used to optimize the SVM parameters, and simultaneously select the optimal subset of features; Finally, single parameter optimization is performed using GA and particle swarm optimization (PSO), and the optimization performance of which is compared with that of GA-PCA and PSO-PCA. Experimental results show that the proposed algorithm can be used to quickly obtain suitable feature subsets and SVM parameters, thereby achieving a better classification result. Hospitalization expense Genetic algorithm SVM Feature selection Parameter optimization Huiling, Lu verfasserin aut Wenwen, Wang verfasserin aut Xia, Yong verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 75, Seite 323-332 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:75 pages:323-332 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 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_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_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 54.00 Informatik: Allgemeines AR 75 323-332 |
| allfieldsGer |
10.1016/j.asoc.2018.11.001 doi (DE-627)ELV001353381 (ELSEVIER)S1568-4946(18)30626-4 DE-627 ger DE-627 rda eng 004 DE-600 54.00 bkl Tao, Zhou verfasserin (orcid)0000-0002-8145-712X aut GA-SVM based feature selection and parameter optimization in hospitalization expense modeling 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Feature selection and parameter optimization are two important aspects to improve the performance of classifier. A novel approach based on the genetic algorithm(GA) for feature selection and parameter optimization of support vector machine(SVM) is proposed in order to improve the prediction accuracy of hospitalization expense model. First of all, the data of hospitalization expense are preprocessed, including data cleaning, discretization, normalization; Secondly, using k-means to cluster and obtain two category labels; Thirdly, kernel penalty factor c, kernel function γ and the feature mask are used to construct chromosome; The Fourth, a weighted combination of classification accuracy and feature number are taken as the fitness function, and GA was used to optimize the SVM parameters, and simultaneously select the optimal subset of features; Finally, single parameter optimization is performed using GA and particle swarm optimization (PSO), and the optimization performance of which is compared with that of GA-PCA and PSO-PCA. Experimental results show that the proposed algorithm can be used to quickly obtain suitable feature subsets and SVM parameters, thereby achieving a better classification result. Hospitalization expense Genetic algorithm SVM Feature selection Parameter optimization Huiling, Lu verfasserin aut Wenwen, Wang verfasserin aut Xia, Yong verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 75, Seite 323-332 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:75 pages:323-332 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 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_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_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 54.00 Informatik: Allgemeines AR 75 323-332 |
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GA-SVM based feature selection and parameter optimization in hospitalization expense modeling |
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(DE-627)ELV001353381 (ELSEVIER)S1568-4946(18)30626-4 |
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GA-SVM based feature selection and parameter optimization in hospitalization expense modeling |
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Tao, Zhou |
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Applied soft computing |
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Applied soft computing |
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eng |
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000 - Computer science, information & general works |
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2018 |
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Tao, Zhou Huiling, Lu Wenwen, Wang Xia, Yong |
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Elektronische Aufsätze |
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Tao, Zhou |
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10.1016/j.asoc.2018.11.001 |
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(ORCID)0000-0002-8145-712X |
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(orcid)0000-0002-8145-712X |
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004 |
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verfasserin |
| title_sort |
ga-svm based feature selection and parameter optimization in hospitalization expense modeling |
| title_auth |
GA-SVM based feature selection and parameter optimization in hospitalization expense modeling |
| abstract |
Feature selection and parameter optimization are two important aspects to improve the performance of classifier. A novel approach based on the genetic algorithm(GA) for feature selection and parameter optimization of support vector machine(SVM) is proposed in order to improve the prediction accuracy of hospitalization expense model. First of all, the data of hospitalization expense are preprocessed, including data cleaning, discretization, normalization; Secondly, using k-means to cluster and obtain two category labels; Thirdly, kernel penalty factor c, kernel function γ and the feature mask are used to construct chromosome; The Fourth, a weighted combination of classification accuracy and feature number are taken as the fitness function, and GA was used to optimize the SVM parameters, and simultaneously select the optimal subset of features; Finally, single parameter optimization is performed using GA and particle swarm optimization (PSO), and the optimization performance of which is compared with that of GA-PCA and PSO-PCA. Experimental results show that the proposed algorithm can be used to quickly obtain suitable feature subsets and SVM parameters, thereby achieving a better classification result. |
| abstractGer |
Feature selection and parameter optimization are two important aspects to improve the performance of classifier. A novel approach based on the genetic algorithm(GA) for feature selection and parameter optimization of support vector machine(SVM) is proposed in order to improve the prediction accuracy of hospitalization expense model. First of all, the data of hospitalization expense are preprocessed, including data cleaning, discretization, normalization; Secondly, using k-means to cluster and obtain two category labels; Thirdly, kernel penalty factor c, kernel function γ and the feature mask are used to construct chromosome; The Fourth, a weighted combination of classification accuracy and feature number are taken as the fitness function, and GA was used to optimize the SVM parameters, and simultaneously select the optimal subset of features; Finally, single parameter optimization is performed using GA and particle swarm optimization (PSO), and the optimization performance of which is compared with that of GA-PCA and PSO-PCA. Experimental results show that the proposed algorithm can be used to quickly obtain suitable feature subsets and SVM parameters, thereby achieving a better classification result. |
| abstract_unstemmed |
Feature selection and parameter optimization are two important aspects to improve the performance of classifier. A novel approach based on the genetic algorithm(GA) for feature selection and parameter optimization of support vector machine(SVM) is proposed in order to improve the prediction accuracy of hospitalization expense model. First of all, the data of hospitalization expense are preprocessed, including data cleaning, discretization, normalization; Secondly, using k-means to cluster and obtain two category labels; Thirdly, kernel penalty factor c, kernel function γ and the feature mask are used to construct chromosome; The Fourth, a weighted combination of classification accuracy and feature number are taken as the fitness function, and GA was used to optimize the SVM parameters, and simultaneously select the optimal subset of features; Finally, single parameter optimization is performed using GA and particle swarm optimization (PSO), and the optimization performance of which is compared with that of GA-PCA and PSO-PCA. Experimental results show that the proposed algorithm can be used to quickly obtain suitable feature subsets and SVM parameters, thereby achieving a better classification result. |
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| title_short |
GA-SVM based feature selection and parameter optimization in hospitalization expense modeling |
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| author2 |
Huiling, Lu Wenwen, Wang Xia, Yong |
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| up_date |
2024-07-06T21:05:12.586Z |
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