MS3A: Wrapper-Based Feature Selection with Multi-swarm Salp Search Optimization

Abstract

Feature selection is crucial in improving the effectiveness of classification or clustering algorithms as a large feature set can affect classification accuracy and learning time. The feature selection process includes choosing the most pertinent features from an initial feature set. This work introduces a new feature selection technique using salp swarm algorithm. In particular, an improved variation of the salp swarm algorithm is presented with modifications done to different stages of the algorithm. The proposed work is evaluated by first studying its performance on standard CEC optimization benchmarks. In addition to this, the applicability of the introduced algorithm for feature selection problems is verified by comparing its performance with existing feature selection algorithms. The experimental analysis depicts that the proposed methodology achieves performance improvement over existing algorithms for both numerical optimization and feature selection problems and reduces the feature subset size by 39.1% when compared to the traditional salp swarm algorithm.