Boosted support vector machines with genetic selection-Reference-Cited by-同舟云学术

Boosted support vector machines with genetic selection

Published:2022-06-17 Issue: Volume: Page:
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Ramirez-Morales A.^ORCID,Salmon-Gamboa J. U.,Li Jin^ORCID,Sanchez-Reyna A. G.,Palli-Valappil A.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-022-03712-z.pdf

Reference49 articles.

1. Zhang C, Ma YE (2012) Ensemble Machine Learning. Springer, https://doi.org/10.1007/978-1-4419-9326-7

2. Sagi O, Rokach L (2018) Ensemble learning: a survey. WIREs Data Min Knowl Discov 8(4):1249. https://doi.org/10.1002/widm.1249

3. Schapire RE (2003) The boosting approach to machine learning: an overview, pp 149–171. Springer, New York. https://doi.org/10.1007/978-0-387-21579-2_9

4. Wang W, Sun D (2021) The improved adaboost algorithms for imbalanced data classification. Inf Sci 563:358–374 . https://doi.org/10.1016/j.ins.03.042

5. Wyner AJ, Olson M, Bleich J, Mease D (2017) Explaining the success of adaboost and random forests as interpolating classifiers. J Mach Learn Res 18:48–14833

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Research on the Disaster Management of China’s Ethnic Minority Autonomous Regions in the Development of Modernization Construction—Taking Mabian Yi Autonomous County in Southern Sichuan as an Example;Sustainability;2024-08-21

2. Ensembles of evolutionarily-constructed support vector machine cascades;Knowledge-Based Systems;2024-03