Binary ant lion approaches for feature selection

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In this paper, binary variants of the ant lion optimizer (ALO) are proposed and used to select the optimal feature subset for classification purposes in wrapper-mode. ALO is one of the recently bio-inspired optimization techniques that imitates the hunting process of ant lions. Moreover, ALO balances exploration and exploitation using a single operator that can adaptively searches the domain of solutions for the optimal solution. Binary variants introduced here are performed using two different approaches. The first approach takes only the inspiration of ALO operators and makes the corresponding binary operators. In the second approach, the native ALO is applied while its continuous steps are threshold using suitable threshold function after squashing them. The proposed approaches for binary ant lion optimizer (BALO) are utilized in the feature selection domain for finding feature subset that maximizing the classification performance while minimizing the number of selected features. The proposed binary algorithms were compared to three common optimization algorithms hired in this domain namely particle swarm optimizer (PSO), genetic algorithms (GAs), binary bat algorithm (BBA), as well as the native ALO. A set of assessment indicators is used to evaluate and compare the different methods over 21 data sets from the UCI repository. Results prove the capability of the proposed binary algorithms to search the feature space for optimal feature combinations regardless of the initialization and the used stochastic operators.