Multi-armed bandit based feature selection

Abstract

Effective feature selection can help reduce dimensionality, improve prediction accuracy, and increase result comprehensibility. Classic feature selection methods typically select and test feature subset in multiple iterations, and thus can be regarded as an exploratory process. In recent literature, the multi-armed bandit has become an emerging method to automate exploration for searching optimal solutions in large spaces. In this paper, our research question is: Can the multi-armed bandit formulation help us to automate feature selection? Along this line, we reformulate the feature selection problem with the combinatorial multi-armed bandit (CMAB) framework by regarding each feature as an arm. We propose two novel oracles and investigate how the super arm is formed under different oracles, and how the coordination between various features can be improved by a novel reward scheme. We present extensive experimental results to demonstrate the improved performance of the proposed methods over conventional feature selection approaches.