An improved relief feature selection algorithm based on Monte-Carlo tree search

Abstract

The goal of feature selection methods is to find the optimal feature subset by eliminating irrelevant or redundant information from the original feature space according to some evaluation criteria. In the literature, the Relief algorithm is a typical feature selection method, which is simple and easy to execute. However, the classification accuracy of the Relief algorithm is usually affected by the noise. In recent years, the Monte Carlo Tree Search (MCTS) technique has achieved great success in strategy selections of large-scale systems by building a tree and quickly focusing on the most valuable part of the search space. In this paper, with the benefit of MCTS, an MCTS-based feature selection approach is proposed to deal with the feature selection problem of high dimensional data, where the Relief algorithm is used as the evaluation function of the MCTS approach. The effectiveness of the proposed approach is demonstrated by experiments on some benchmark problems.