AutoGFS: Automated group-based feature selection via interactive reinforcement learning

Abstract

Feature selection is a fundamental component of data mining, aiming to select optimal feature subsets for downstream task. Recently, an emerging feature selection method called reinforced feature selection applies reinforcement learning into feature selection. Reinforced Feature Selection (RFS) automates feature selection process and can effectively find the optimal subset. Generally, RFS can be categorized into single-agent RFS and multi-agent RFS. Single-agent RFS uses one reinforcement learning agent to select features, but its action space is exponentially-increasing with feature number and can merely obtain local optima. Multi-agent RFS uses multiple agents to select features; this method can achieve global optima, but it needs to optimize as many policy networks as feature number which costs huge computational resources and thus becomes computationally inefficient. This dilemma naturally leads to a research question: How can we synthesize the advantages of single-agent RFS and multi-agent RFS while avoiding their disadvantages? To answer this question, we propose a Group-based Interactive Reinforced Feature Selection (GIRFS) framework. This framework balances single-agent RFS and multi-agent RFS for better feature selection. Specifically, we formulate the feature selection problem into a group-based RFS problem. In this formulation, we first assign the given features into several groups based on feature similarity measurement. Then, we create agents for each group, where each agent decides to select/deselect features in its corresponding group. This design balances the size of action space and number of policy networks and thus makes RFS more effective and efficient. Moreover, to further improve learning efficiency, we propose a hierarchical teacher-like trainer to provide external action advice for agents. This trainer provides advice by intra-group selection and inter-group selection and fuses knowledge from mRMR and decision tree to help agents explore and learn. Finally, we present extensive experiments on real-world datasets to demonstrate the improved performances of our method.