A novel multi-classifier based on a density-dependent quantized binary tree LSSVM and the logistic global whale optimization algorithm-Reference-Cited by-同舟云学术

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A novel multi-classifier based on a density-dependent quantized binary tree LSSVM and the logistic global whale optimization algorithm

Published:2020-06-30 Issue:11 Volume:50 Page:3808-3821
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Chen Jiaoliao,Zhuo Xingai,Xu Fang,Wang Jiacai,Zhang Dan,Zhang Libin

Funder

Zhejiang Province Public Welfare Technology Application Research Project

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

http://link.springer.com/content/pdf/10.1007/s10489-020-01736-x.pdf

Reference49 articles.

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2. Wang JX, Tan DP, Cao B, Fan J, Deep S (2020) Independent path-based process recommendation algorithm for improving biomedical process modeling. Electronics Letter 2:1–13. https://doi.org/10.1049/el.2019.3978

3. Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Letters 9(3):293–300. https://doi.org/10.1023/a:1018628609742

4. Tang JJ, Liu F, Zou YJ, Zhang WB, Wang YH (2017) An improved fuzzy neural network for traffic speed prediction considering periodic characteristic. IEEE Trans Intell Transpo Sys 18(9):2340–2350. https://doi.org/10.1109/tits.2016.2643005

5. Ticay-Rivas JR, del Pozo-Banos M, Eberhard WG, Alonso JB, Travieso CM (2013) Spider specie identification and verification based on pattern recognition of it cobweb. Expert Syst Appl 40 (10):4213–4225. https://doi.org/10.1016/j.eswa.2013.01.024

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