Correlation Feature Selection Weighting Algorithms for Better Support Vector Classification: An Empirical Study
Characteristics of Support Vector Machine (SVM) and its classifications are elaborated to show why incorporation of newly proposed and formulated regularization on feature selections based on correlation studies are necessary to achieve a better prediction or classification. Feature selections based...
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| Format: | Article |
| Language: | en |
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Solid State Technology
2020
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| Online Access: | http://ir.unimas.my/id/eprint/32921/1/CORRELATION%20FEATURE.pdf http://ir.unimas.my/id/eprint/32921/ http://solidstatetechnology.us/index.php/JSST |
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| author | Sim, Doreen Ying Ying Teh, Chee Siong Ahmad Izuanuddin, Ismail |
| author_facet | Sim, Doreen Ying Ying Teh, Chee Siong Ahmad Izuanuddin, Ismail |
| author_sort | Sim, Doreen Ying Ying |
| building | Centre for Academic Information Services (CAIS) |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Sarawak |
| content_source | UNIMAS Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Characteristics of Support Vector Machine (SVM) and its classifications are elaborated to show why incorporation of newly proposed and formulated regularization on feature selections based on correlation studies are necessary to achieve a better prediction or classification. Feature selections based on correlation studies are incorporated into the proposed formulations for the weighting portions of the objective functions for SVM. Proposed cfsw-SVM algorithms are then developed. Proposed formulations on SVM regularization parameter provides synergistic adjustments between prediction or classification accuracy and the level of correlations among features in the SVM implemented. Prediction and/or classification accuracies of cfsw-SVM algorithms are significantly improved. |
| format | Article |
| id | my.unimas.ir-32921 |
| institution | Universiti Malaysia Sarawak |
| language | en |
| publishDate | 2020 |
| publisher | Solid State Technology |
| record_format | eprints |
| spelling | my.unimas.ir-329212022-08-18T07:46:03Z http://ir.unimas.my/id/eprint/32921/ Correlation Feature Selection Weighting Algorithms for Better Support Vector Classification: An Empirical Study Sim, Doreen Ying Ying Teh, Chee Siong Ahmad Izuanuddin, Ismail QA75 Electronic computers. Computer science Characteristics of Support Vector Machine (SVM) and its classifications are elaborated to show why incorporation of newly proposed and formulated regularization on feature selections based on correlation studies are necessary to achieve a better prediction or classification. Feature selections based on correlation studies are incorporated into the proposed formulations for the weighting portions of the objective functions for SVM. Proposed cfsw-SVM algorithms are then developed. Proposed formulations on SVM regularization parameter provides synergistic adjustments between prediction or classification accuracy and the level of correlations among features in the SVM implemented. Prediction and/or classification accuracies of cfsw-SVM algorithms are significantly improved. Solid State Technology 2020 Article PeerReviewed text en http://ir.unimas.my/id/eprint/32921/1/CORRELATION%20FEATURE.pdf Sim, Doreen Ying Ying and Teh, Chee Siong and Ahmad Izuanuddin, Ismail (2020) Correlation Feature Selection Weighting Algorithms for Better Support Vector Classification: An Empirical Study. Solid State Technology, 63 (2s). pp. 2794-2805. ISSN 0038-111X http://solidstatetechnology.us/index.php/JSST |
| spellingShingle | QA75 Electronic computers. Computer science Sim, Doreen Ying Ying Teh, Chee Siong Ahmad Izuanuddin, Ismail Correlation Feature Selection Weighting Algorithms for Better Support Vector Classification: An Empirical Study |
| title | Correlation Feature Selection Weighting Algorithms for Better Support Vector Classification: An Empirical Study |
| title_full | Correlation Feature Selection Weighting Algorithms for Better Support Vector Classification: An Empirical Study |
| title_fullStr | Correlation Feature Selection Weighting Algorithms for Better Support Vector Classification: An Empirical Study |
| title_full_unstemmed | Correlation Feature Selection Weighting Algorithms for Better Support Vector Classification: An Empirical Study |
| title_short | Correlation Feature Selection Weighting Algorithms for Better Support Vector Classification: An Empirical Study |
| title_sort | correlation feature selection weighting algorithms for better support vector classification: an empirical study |
| topic | QA75 Electronic computers. Computer science |
| url | http://ir.unimas.my/id/eprint/32921/1/CORRELATION%20FEATURE.pdf http://ir.unimas.my/id/eprint/32921/ http://solidstatetechnology.us/index.php/JSST |
| url_provider | http://ir.unimas.my/ |