© 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.<div><br></div><div><div><table><tr><td>© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.</td></tr></table></div></div>
A Multi-objective Non-Dominated Sorted Artificial Bee Colony Feature Selection Algorithm for Medical Datasets
