<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Feature Selection (FS) is an essential research topic in the area of machine learning. FS, which is the process of identifying the relevant features and removing the irrelevant and redundant ones, is meant to deal with high dimensionality problems to select the best performing feature subset. In the literature, many feature selection techniques approach the task as a research problem, where each state in the search space is a possible feature subset. In this paper, we introduce a new feature selection method based on reinforcement learning. First, decision tree branches are used to traverse the search space. Second, a transition similarity measure is proposed so as to ensure exploit-explore trade-off. Finally, the informative features are the most involved ones in constructing the best branches. The performance of the proposed approaches is evaluated on nine standard benchmark datasets. The results using the AUC score show the effectiveness of the proposed system.</p>
</abstract>

Feature Selection (FS) is an essential research topic in the area of machine learning. FS, which is the process of identifying the relevant features and removing the irrelevant and redundant ones, is meant to deal with high dimensionality problems to select the best performing feature subset. In the literature, many feature selection techniques approach the task as a research problem, where each state in the search space is a possible feature subset. In this paper, we introduce a new feature selection method based on reinforcement learning. First, decision tree branches are used to traverse the search space. Second, a transition similarity measure is proposed so as to ensure exploit-explore trade-off. Finally, the informative features are the most involved ones in constructing the best branches. The performance of the proposed approaches is evaluated on nine standard benchmark datasets. The results using the AUC score show the effectiveness of the proposed system.

Using Reinforcement Learning to Select an Optimal Feature Set

Department of Informatics, faculty of sciences dhar el mahraz

Department of Informatics, faculty of sciences

Journal of Automation, Mobile Robotics and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Using Reinforcement Learning to Select an Optimal Feature Set"}

Feature Selection (FS) is an essential research topic in the area of machine learning. FS, which is the process of identifying the relevant features and...

Using Reinforcement Learning to Select an Optimal Feature Set

Online veröffentlicht: 13. Apr. 2024

Seitenbereich: 56 - 66

Eingereicht: 23. Feb. 2022

Akzeptiert: 25. Jan. 2023

DOI: https://doi.org/10.14313/jamris/1-2024/6

Schlüsselwörter
Feature selection, Data mining, Decision tree, Reinforcement learning, Dimensionality reduction

© 2024 Yassine Akhiat et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Using Reinforcement Learning to Select an Optimal Feature Set

Online veröffentlicht: 13. Apr. 2024

Seitenbereich: 56 - 66

Eingereicht: 23. Feb. 2022

Akzeptiert: 25. Jan. 2023

DOI: https://doi.org/10.14313/jamris/1-2024/6

SchlüsselwörterFeature selection, Data mining, Decision tree, Reinforcement learning, Dimensionality reduction

© 2024 Yassine Akhiat et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Feature selection, Data mining, Decision tree, Reinforcement learning, Dimensionality reduction