Related papers: Feature and Instance Joint Selection: A Reinforcement Learning Perspective

Feature and Instance Joint Selection: A Reinforcement Learning Perspective

URL: http://arxiv.org/abs/2205.07867v1
Date: Thu, 12 May 2022 07:51:32 GMT
Title: Feature and Instance Joint Selection: A Reinforcement Learning Perspective
Authors: Wei Fan, Kunpeng Liu, Hao Liu, Hengshu Zhu, Hui Xiong, Yanjie Fu
Abstract summary: We propose a reinforcement learning solution to accomplish the joint selection task. In particular, a sequential-scanning mechanism is designed as action strategy of agents. Experiments on real-world datasets have demonstrated improved performances.
Score: 47.704739699011995
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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