Feature and Instance Joint Selection: A Reinforcement Learning Perspective

TL;DR

This paper introduces a reinforcement learning approach for joint feature and instance selection, capturing their interactions to improve data processing efficiency and effectiveness.

Contribution

It proposes a novel RL-based method with a sequential-scanning mechanism and collaborative environment for fine-grained joint selection.

Findings

Enhanced selection performance on real-world datasets

Effective capture of feature-instance interactions

Improved data processing outcomes

Abstract

Feature selection and instance selection are two important techniques of data processing. However, such selections have mostly been studied separately, while existing work towards the joint selection conducts feature/instance selection coarsely; thus neglecting the latent fine-grained interaction between feature space and instance space. To address this challenge, we propose a reinforcement learning solution to accomplish the joint selection task and simultaneously capture the interaction between the selection of each feature and each instance. In particular, a sequential-scanning mechanism is designed as action strategy of agents, and a collaborative-changing environment is used to enhance agent collaboration. In addition, an interactive paradigm introduces prior selection knowledge to help agents for more efficient exploration. Finally, extensive experiments on real-world datasets have demonstrated improved performances.