A binary ABC algorithm based on advanced similarity scheme for feature selection

Graphical abstractAn illustrative representation on how the features are selected. Display Omitted HighlightsA binary artificial bee colony (ABC) algorithm is proposed for feature selection.A comprehensive comparative study of the ABC PSO variants is presented.The superiority of the algorithm is demonstrated on both training and test sets.The times of appearance of each feature over 30 runs for each dataset are presented.The proposed algorithm performs better than the others. Feature selection is the basic pre-processing task of eliminating irrelevant or redundant features through investigating complicated interactions among features in a feature set. Due to its critical role in classification and computational time, it has attracted researchers' attention for the last five decades. However, it still remains a challenge. This paper proposes a binary artificial bee colony (ABC) algorithm for the feature selection problems, which is developed by integrating evolutionary based similarity search mechanisms into an existing binary ABC variant. The performance analysis of the proposed algorithm is demonstrated by comparing it with some well-known variants of the particle swarm optimization (PSO) and ABC algorithms, including standard binary PSO, new velocity based binary PSO, quantum inspired binary PSO, discrete ABC, modification rate based ABC, angle modulated ABC, and genetic algorithms on 10 benchmark datasets. The results show that the proposed algorithm can obtain higher classification performance in both training and test sets, and can eliminate irrelevant and redundant features more effectively than the other approaches. Note that all the algorithms used in this paper except for standard binary PSO and GA are employed for the first time in feature selection.