Moth-Flame Optimization Algorithm for Feature Selection: A Review and Future Trends

Abstract

Feature selection is a machine learning technique that aims to select a small subset of features from a given high dimensional datasets which usually involves irrelevant or redundant features. In machine learning, performing feature selection becomes a vital task to achieve high classification accuracy and minimize the computational cost. However, feature selection is a challenging task due to the complex interaction that exists between features and the massive search space. Numerous optimization algorithms have been employed to tackle this problem of which Moth-Flame Optimization (MFO) algorithm is considered one of the most promising metaheuristic algorithms that have been successfully applied to solve feature selection problems. This chapter reviews the recent studies on feature selection using MFO. Researchers and practitioners of MFO belonging to feature selection domain will benefit from this study.