Adaptive SVM-Based classification systems based on the improved endocrine-based PSO algorithm

Authors:
Kuan-Cheng Lin;Sheng-Hwa Hsu;Jason C. Hung
Affiliations:
Department of Management Information Systems, National Chung Hsing University, Taichung, Taiwan;Department of Management Information Systems, National Chung Hsing University, Taichung, Taiwan;Department of Information Management, Overseas Chinese University, Taichung, Taiwan
Venue:
AMT'12 Proceedings of the 8th international conference on Active Media Technology
Year:
2012

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Abstract

In this study, we proposed an wrapped feature selection and SVM's kernel parameters optimization scheme using Improved Artificial Endocrine System to get an optimal support vector machines classification system. By taking the advantage of the mechanisms of hormone action in Artificial Endocrine System, we can avoid to obtain local optimums and oscillations. We used the UCI database to evaluate the performance of the proposed scheme with the previous methods. The experiment results indicated that the proposed scheme can avoid local optimum and also reduce feature numbers significantly with a good-enough accuracy in high-complexity datasets. Moreover, by decreasing the number of unnecessary features, we can even improve the accuracy of classification.