Feature Selection Approach based on Moth-Flame Optimization Algorithm

In this work, a feature selection algorithm based on moth-flame optimization (MFO) is proposed. Moth-flame optimization (MFO) is a recently proposed swarm intelligent optimization algorithm that mimics the motion of moths. The proposed algorithm is applied in the domain of machine learning for feature selection to find the optimal feature combination using wrapper-based feature selection mode. In wrapper-based feature selection, a machine learning technique is used in the evaluation step. Despite it is very costly in time, this technique proved to have a good performance in classification accuracy. MFO is exploited in this study as a searching method to find optimal feature set, maximizing classification performance. The proposed algorithm is compared against particle swarm optimization (PSO) and genetic algorithms (GA). A set of UCI data sets is used for comparison using different assessment indicators. Results prove the efficiency of the proposed algorithm in comparison to other algorithms.