A Hybrid Principal Label Space Transformation-Based Binary Relevance Support Vector Machine and Q-Learning Algorithm for Multi-label Classification-Reference-Cited by-同舟云学术

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A Hybrid Principal Label Space Transformation-Based Binary Relevance Support Vector Machine and Q-Learning Algorithm for Multi-label Classification

Published:2024-04-20 Issue: Volume: Page:
ISSN:2193-567X
Container-title:Arabian Journal for Science and Engineering
language:en
Short-container-title:Arab J Sci Eng

Author:

Ebrahimi Seyed Hossein Seyed

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s13369-024-09034-1.pdf

Reference31 articles.

1. Sarker, I.H.: Machine learning: algorithms, real-world applications and research directions. SN Comput. Sci. 2(3), 160 (2021)

2. Mahesh, B.: Machine learning algorithms-a review. Int. J. Sci. Res. (IJSR) 9(1), 381–386 (2020)

3. Amin, J., et al.: Brain tumor detection and classification using machine learning: a comprehensive survey. Complex Intell. Syst. 8(4), 3161–3183 (2022)

4. Sen, P.C.; Hajra, M., Ghosh. M.: Supervised classification algorithms in machine learning: a survey and review. In: Emerging Technology in Modelling and Graphics: Proceedings of IEM Graph 2018. Springer (2020)

5. Seyed Ebrahimi, S.H.; Majidzadeh, K.; Soleimanian Gharehchopogh, F.: A novel learning-based PLST algorithm for multi-label classification. IETE J. Res. 1–19 (2023)

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