Reinforced feature selection using Q-learning based on collaborative agents-Reference-Cited by-同舟云学术

Reinforced feature selection using Q-learning based on collaborative agents

Published:2023-05-31 Issue:11 Volume:14 Page:3867-3882
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Zhang Li^ORCID,Jin Lingbin,Gan Min,Zhao Lei,Yin Hongwei

Funder

Natural Science Foundation of the Jiangsu Higher Education Institutions of China

Six Talent Peak Project of Jiangsu Province of China

Collaborative Innovation Center of Novel Software Technology and Industrialization

Priority Academic Program Development of Jiangsu Higher Education Institutions

Huzhou Public Welfare Application research project

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s13042-023-01869-8.pdf

Reference28 articles.

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2. Alharin A, Doan TN, Sartipi M (2020) Reinforcement learning interpretation methods: a survey. IEEE Access 8:171,058-171,077

3. Arauzo-Azofra A, Jiménez-Vílchez A, Molina-Baena J et al (2019) Algorithmic cache of sorted tables for feature selection - Speeding up methods based on consistency and information theory measures. Data Min Knowl Discov 33(4):964–994. https://doi.org/10.1007/s10618-019-00620-8

4. Borboudakis G, Tsamardinos I (2021) Extending greedy feature selection algorithms to multiple solutions. Data Min Knowl Disc 35(4):1393–1434

5. Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput Electr Eng 40(1):16–28

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