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.

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.

A new model is developed for joint analysis of ordered, categorical, real and count data. The ordered and categorical data are answers to questionnaires, the (word) count data correspond to the text questions from the questionnaires, and the real data correspond to fMRI responses for each subject. The Bayesian model employs the von Mises distribution in a novel manner to infer sparse graphical models jointly across people, questions, fMRI stimuli and brain region, with this integrated within a new matrix factorization based on latent binary features. The model is compared with simpler alternatives on two real datasets. We also demonstrate the ability to predict the response of the brain to visual stimuli (as measured by fMRI), based on knowledge of how the associated person answered classical questionnaires.