Binary Firefly Algorithm for Feature Selection in Classification

Abstract

The large dimension of datasets influences on the classification performances and computational time. For that, feature selection is among process that avoid of those problems by selecting the relevance and eliminate the redundancy features.In this paper, we propose a new algorithm for feature selection FAFS (Firefly Algorithm for Feature Selection) which is based on the firefly algorithm. FAFS uses two objectives, which are Accuracy Rate and Reduction Rate. We propose a new formula to calculate the distance r and attractive A in Firefly algorithm.The experimental results show the capability of the proposed algorithm with three classifiers (KNN, NB, and LDA) and their outperformance against PSO-FS (Particle Swarm Optimization for feature selection).