A novel framework based on the multi-label classification for dynamic selection of classifiers-Reference-Cited by-同舟云学术

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A novel framework based on the multi-label classification for dynamic selection of classifiers

Published:2023-01-02 Issue:6 Volume:14 Page:2137-2154
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Elmi Javad,Eftekhari Mahdi,Mehrpooya Adel,Ravari Mohammad Rezaei

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/s13042-022-01751-z.pdf

Reference47 articles.

1. Balogun A, Bajeh A, Mojeed H, Akintola A (2020) Software defect prediction: a multi-criteria decision-making approach. Nigerian J Tech Res 15(1):35–42

2. Bedi P, Gupta N, Jindal V (2020) I-siamids: an improved siam-ids for handling class imbalance in network-based intrusion detection systems. Appl Intell 51:1133–1151

3. Bhatore S, Mohan L, Reddy YR (2020) Machine learning techniques for credit risk evaluation: a systematic literature review. J Banking Fin Tech 4:111–138

4. Britto S Jr, A., Sabourin R, Soares de Oliveira L, (2014) Dynamic selection of classifiers-a comprehensive review. Pattern Recogn 47:3665–3680. https://doi.org/10.1016/j.patcog.2014.05.003

5. Cavalin PR, Sabourin R, Suen CY (2013) Dynamic selection approaches for multiple classifier systems. Neural Comput Appl 22(3):673–688

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