Correlation Feature Selection Weighting Algorithms for Better Support Vector Classification: An Empirical Study

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

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: Support Vector Machine (SVM), classifications, algorithms, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, research, Universiti Malaysia Sarawak
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Academic Faculties, Institutes and Centres > Faculty of Cognitive Sciences and Human Development
Depositing User: Siong
Date Deposited: 25 Nov 2020 08:02
Last Modified: 25 Nov 2020 08:02
URI: http://ir.unimas.my/id/eprint/32921

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