Swarm MeLiF: Feature selection with filter combination found via swarm intelligence

Abstract

Combination of algorithms being called ensemble is a widely used machine learning technique. In this paper we propose a new method Swarm MeLiF which aims to find the best combination of basic filters and uses swarm optimization methods for this purpose. In this work we combine filters by combining the measures they use to evaluate feature importance. Thus, the problem of filter ensemble learning is reduced to finding a linear combination of these measures. We applied several swarm optimization methods and found that Particle Swarm Optimization shows the best results and outperforms the original MeLiF.