Hancer 2015 Multi-objective artificial bee colony approach to feature.pdf (764.7 kB)

A multi-objective artificial bee colony approach to feature selection using fuzzy mutual information

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conference contribution
posted on 16.03.2021, 02:07 by E Hancer, Bing Xue, Mengjie Zhang, D Karaboga, B Akay
© 2015 IEEE. Feature selection often involves two conflicting objectives of minimizing the feature subset size and the maximizing the classification accuracy. In this paper, a multi-objective artificial bee colony (MOABC) framework is developed for feature selection in classification, and a new fuzzy mutual information based criterion is proposed to evaluate the relevance of feature subsets. Three new multi-objective feature selection approaches are proposed by integrating MOABC with three filter fitness evaluation criteria, which are mutual information, fuzzy mutual information and the proposed fuzzy mutual information. The proposed multi-objective feature selection approaches are examined by comparing them with three single-objective ABC-based feature selection approaches on six commonly used datasets. The results show that the proposed approaches are able to achieve better performance than the original feature set in terms of the classification accuracy and the number of features. By using the same evaluation criterion, the proposed multi-objective algorithms generally perform better than the single-objective methods, especially in terms of reducing the number of features. Furthermore, the proposed fuzzy mutual information criterion outperforms mutual information and the original fuzzy mutual information in both single-objective and multi-objective manners. This work is the first study on multi-objective ABC for filter feature selection in classification, which shows that multi-objective ABC can be effectively used to address feature selection problems.

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Preferred citation

Hancer, E., Xue, B., Zhang, M., Karaboga, D. & Akay, B. (2015, September). A multi-objective artificial bee colony approach to feature selection using fuzzy mutual information. In 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings 2015 IEEE Congress on Evolutionary Computation (CEC), Sendai, JAPAN (pp. 2420-2427). IEEE. https://doi.org/10.1109/CEC.2015.7257185

Conference name

2015 IEEE Congress on Evolutionary Computation (CEC)

Conference Place

Sendai, JAPAN

Conference start date

25/05/2015

Conference finish date

28/05/2015

Title of proceedings

2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

Series

IEEE Congress on Evolutionary Computation

Publication or Presentation Year

10/09/2015

Pagination

2420-2427

Publisher

IEEE

Publication status

Published

Exports