A synergy Thompson sampling hyper‐heuristic for the feature selection problem. (26th April 2020)

• Record Type: Journal Article
• Authors: Lassouaoui, Mourad; Boughaci, Dalila; Benhamou, Belaid
• Is Part Of: Computational intelligence. Volume 38:Number 3(2022)
• Page Start: 1083
• Page End: 1105
• Publication Date: 2020-04-26
• Subjects: classification -- combinatorial optimization -- feature selection -- hyper‐heuristics -- machine learning -- Thompson sampling; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12325
• Languages: English
• ISSNs: 0824-7935

Abstract

Summary: To classify high‐dimensional data, feature selection plays a key role to eliminate irrelevant attributes and enhance the classification accuracy and efficiency. Since feature selection is an NP‐Hard problem, many heuristics and metaheuristics have been used to tackle in practice this problem. In this article, we propose a novel approach that consists in a probabilistic selection hyper‐heuristic called the synergy Thompson sampling hyper‐heuristic. The Thompson sampling selection strategy is a probabilistic reinforcement learning mechanism to assess the behavior of the low‐level heuristics, and to predict which one will be more efficient at each point during the search process. The proposed hyper‐heuristic is combined with a 1 nearest neighbor classifier from the Weka framework. It aims to find the best subset of features that maximizes the classification accuracy rate. Experimental results show a good performance in favor of the proposed method when comparing with other existing approaches.