MAB-optimized binary PSO-based feature selection for enhanced classification performance

Abstract

This paper proposes a novel algorithm built on a reinforcement learning approach as an alternative to standard hyperparameter tuning methods for improving the feature selection process to obtain better classification performance. Various techniques have been proposed to handle the problem, and among them are metaheuristics, such as Particle Swarm Optimization (PSO). However, a new problem arises as learning algorithms must often be tuned in terms of their hyperparameters. We need to solve this problem separately, and vary of methods can be used for this matter. The hyperparameter optimization for the learning process of PSO can be seen as a sequence decision-making process. Hence, we can solve it with the multi-armed bandit problem algorithms.