Grey wolf algorithm with borda count for feature selection in classification

This research applies the grey wolf optimization algorithm which includes the feature selection stage and implements borda count method to optimize the function selection problem in classification. The grey wolf optimization mimics the characteristics and movement of wolves which have more than one leader in a pack. The proposed algorithm presents the pack can have more than one pack which selects the most relevant features. The performance of proposed algorithm is compared with other classification techniques such as cAnt-Miner, C4.5 and PART. The experimental results show that the proposed algorithm is capable to optimized feature selection in classification problems.