A comparative analysis of meta-heuristic optimization algorithms for feature selection and feature weighting in neural networks

Feature selection and feature weighting are frequently used in machine learning for processing high dimensional data. It reduces the number of features in the dataset and makes the classification process easier. Meta-heuristic algorithms are widely adopted for feature selection and feature weighting due to their enhanced searching ability. This paper compares five different meta-heuristic optimization algorithms that are recently introduced for feature selection and feature weighting in artificial neural networks. This includes chimp optimization algorithm, tunicate swarm algorithm, bear smell search algorithm, antlion optimization algorithm and modified antlion optimization algorithm. Experimental evaluations are performed on five different datasets to illustrate the significant improvements observed during classification process of all the algorithms utilised in the comparative analysis. Both tunicate swarm algorithm and chimp optimization algorithm has gained better classification accuracy than other algorithms. However, all these algorithms are found to be more effective for feature selection and feature weighting processes.