Abstract
Feature selection is known as an NP-hard combinatorial problem in which the possible feature subsets increase exponentially with the number of features. Due to the increment of the feature size, the exhaustive search has become impractical. In addition, a feature set normally includes irrelevant, redundant, and relevant information. Therefore, in this paper, binary variants of a competitive swarm optimizer are proposed for wrapper feature selection. The proposed approaches are used to select a subset of significant features for classification purposes. The binary version introduced here is performed by employing the S-shaped and V-shaped transfer functions, which allows the search agents to move on the binary search space. The proposed approaches are tested by using 15 benchmark datasets collected from the UCI machine learning repository, and the results are compared with other conventional feature selection methods. Our results prove the capability of the proposed binary version of the competitive swarm optimizer not only in terms of high classification performance, but also low computational cost.
Highlights
In recent days, many applications involve the role of extracting useful information for data collection
Unlike the S-shaped transfer function, the V-shaped transfer function does not force the search agents to move on the binary search space
It is observed that binary version of the competitive swarm optimizer (BCSO) provided competitive performance against binary particle swarm optimization (BPSO), genetic algorithm (GA), binary salp swarm algorithm (BSSA), and binary differential evolution (BDE)
Summary
Many applications involve the role of extracting useful information for data collection. Feature selection can be classified into two approaches: filter and wrapper The former identifies the relevant features by using the proxy measure, mutual information, and data characteristics, while the later utilizes a predictive model to train the feature set for evaluating the nearly optimal feature subset [5,6]. The most common wrapper feature selection methods are the genetic algorithm (GA) and binary particle swarm optimization (BPSO). Unlike GA, BPSO is a swarm-based algorithm that generates the population of solutions called particles.
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