A Deep Reinforcement Learning Based Feature Selector

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In the field of data mining and machine learning, it is a challenge for researchers and engineers to analyze and classify the high-dimensional data. In order to minimize the classification error, it is critical to identify and select the most characterizing features as well as remove the irrelevant features from the high-dimensional data. Feature selection algorithms provide effective processes to find a compact valuable feature subset from the set of all the candidate features. In past researches, most feature selection methods treat such a problem as a scoring problem, where each feature is evaluated individually and top-ranked features are selected. In this study, adding new features into the feature subset is considered as a Markov Decision Process, and the Feature Selection task is formalized as a reinforcement learning problem. This paper proposes a novel Deep Reinforcement Learning based Feature Selector (DRLFS) along with a dynamic randomness policy and two search protocols to tackle the exploration versus exploitation dilemma for a gradual approach to the optimum subset. The experimental results on various benchmark datasets prove the promising feature selection ability of the proposal of this paper.

Original languageEnglish
Title of host publication11th International Symposium, PAAP 2020, Proceedings
EditorsLi Ning, Vincent Chau, Francis Lau
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9789811600098
Publication statusPublished - 2021
Event11th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2020 - Shenzhen, China
Duration: 2020 Dec 282020 Dec 30

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference11th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2020


  • AutoML
  • Data mining
  • Feature selection
  • High-dimensional data
  • Reinforcement learning

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)


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