A New Multi-classifier Ensemble Algorithm Based on D-S Evidence Theory-Reference-Cited by-同舟云学术

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A New Multi-classifier Ensemble Algorithm Based on D-S Evidence Theory

Published:2022-06-09 Issue:6 Volume:54 Page:5005-5021
ISSN:1370-4621
Container-title:Neural Processing Letters
language:en
Short-container-title:Neural Process Lett

Author:

Zhao Kaiyi,Li Li,Chen Zeqiu,Sun Ruizhi^ORCID,Yuan Gang,Li Jiayao

Funder

Application of collaborative precision positioning service for mass users

National Development and Reform Commission integrated data service system infrastructure platform construction project

Guangxi Key Lab of Multi-source Information Mining & Security

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software

Link

https://link.springer.com/content/pdf/10.1007/s11063-022-10845-2.pdf

Reference43 articles.

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2. Campos GO, Zimek A, Sander J, Campello R, Micenková B, Schubert E, Assent I, Houle ME (2015) On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Min Knowl Disc 30:891–927

3. Chen Q, Shi L, Na J, Ren X, Nan Y (2018) Adaptive echo state network control for a class of pure-feedback systems with input and output constraints. Neurocomputing 275:1370–1382. https://doi.org/10.1016/j.neucom.2017.09.083

4. Chen W, Li Y, Tsangaratos P, Shahabi H, Ilia I, Xue W, Bian H (2020) Groundwater spring potential mapping using artificial intelligence approach based on kernel logistic regression, random forest, and alternating decision tree models. Appl Sci. https://doi.org/10.3390/app10020425

5. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297. https://doi.org/10.1023/A:1022627411411

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