A Binary Superior Tracking Artificial Bee Colony for Feature Selection

Feature selection is a NP-hard combinatorial problem of selecting the effective features from a given set of original features to reduce the dimension of dataset. This paper aims to propose an improved variant of learning algorithm for feature selection, termed as Binary Superior Tracking Artificial Bee Colony (BST-ABC) algorithm. In BST-ABC, a binary learning strategy is proposed to enable each bee to learn from the superior individuals in each dimension for exploitation capacity enhancement. Ten datasets from UCI repository are adopted as test problems, and the results of BST-ABC are compared with particle swarm optimization (PSO) and original ABC. Experimental results demonstrate that BST-ABC could obtain the optimal classification accuracy and the minimum number of features.

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