Feature selection based on antlion optimization algorithm

In this work, a model for feature selection based on antlion optimization (ALO) is proposed. Feature sets always have redundant, dependant and correlated features that badly affect the classification performance and increases training time. Therefore, feature selection becomes a must to remove irrelevant features and enhances classification generalization. Wrapper-based feature selection is a method that selects a feature set maximizing a given classifier performance criteria and hence requires efficient searching method to find optimal feature combinations. Antlion optimization is a recently proposed swarm optimizer with good searching capability. ALO is exploited in this study as searching method to find optimal feature set maximizing classification performance. ALO algorithm mimics the hunting mechanism of antlions in nature. The proposed model is evaluated using different evaluation criteria on 18 different data sets and is compared to two common search methods namely particle swarm optimization (PSO) and genetic algorithm (GA) and proves an advance in classification performance and selected feature set.