An efficient binary social spider algorithm for feature selection problem

The social spider algorithm (SSA) is a heuristic algorithm created on spider behaviors to solve continuous problems. In this paper, firstly a binary version of the social spider algorithm called binary social spider algorithm (BinSSA) is proposed. Currently, there is insufficient focus on the binary version of SSA in the literature. The main part of the binary version is the transfer function. The transfer function is responsible for mapping continuous search space to binary search space. In this study, eight of the transfer functions divided into two families, S-shaped and V-shaped, are evaluated. BinSSA is obtained from SSA, by transforming constant search space to binary search space with eight different transfer functions (S-Shapes and V-Shaped). Thus, eight different variations of BinSSA are formed as BinSSA1, BinSSA2, BinSSA3, BinSSA4, BinSSA5, BinSSA6, BinSSA7, and BinSSA8. For increasing, exploration and exploitation capacity of BinSSA, a crossover operator is added as BinSSA-CR. In secondly, the performances of BinSSA variations are tested on feature selection task. The optimal subset of features is a challenging problem in the process of feature selection. In this paper, according to different comparison criteria (mean of fitness values, the standard deviation of fitness values, the best of fitness values, the worst of fitness values, accuracy values, the mean number of the selected features, CPU time), the best BinSSA variation is detected. In the feature selection problem, the K-nearest neighbor (K-NN) and support vector machines (SVM) are used as classifiers. A detailed study is performed for the fixed parameter values used in the fitness function. BinSSA is evaluated on low-scaled, middle-scaled and large-scaled twenty-one well-known UCI datasets and obtained results are compared with state-of-art algorithms in the literature. Obtained results have shown that BinSSA and BinSSA-CR show superior performance and offer quality and stable solutions. (C) 2020 Elsevier Ltd. All rights reserved.