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  Fuzzy large margin distribution machine for classification

Dong, D., Feng, M., Kurths, J., Zhang, L. (2024): Fuzzy large margin distribution machine for classification. - International Journal of Machine Learning and Cybernetics, 15, 1891-1905.
https://doi.org/10.1007/s13042-023-02004-3

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 Creators:
Dong, Denghao1, Author
Feng, Minyu1, Author
Kurths, Jürgen2, Author              
Zhang, Libo1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: As a variant of Support Vector Machine (SVM), Large Margin Distribution Machine (LDM) has been validated to outperform SVM both theoretically and experimentally. Due to the inevitable noise in real applications, the credibility of different samples is not necessarily the same, which is neglected by most existing LDM models. To tackle the above problem, this paper first introduces fuzzy set theory into LDM, and proposes a Fuzzy Large Margin Distribution Machine (FLDM) with better robustness and performance. Considering the noise and uncertainty in datasets, sample points farther from the center of homogenous class are less reliable. Therefore, a fuzzy membership function based on the distance to the class center is utilized to characterize the confidence of each sample, i.e., the degree to which the sample belongs to a certain category. Furthermore, different strategies are developed to obtain class centers for linearly separable and linearly inseparable problems. Experiments conducted on both artificial and UCI datasets verified the superiority of FLDM from different perspectives.

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Language(s): eng - English
 Dates: 2023-11-042024-05-01
 Publication Status: Finally published
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1007/s13042-023-02004-3
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Model / method: Machine Learning
 Degree: -

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Title: International Journal of Machine Learning and Cybernetics
Source Genre: Journal, SCI, Scopus
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Pages: - Volume / Issue: 15 Sequence Number: - Start / End Page: 1891 - 1905 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/1868-808X
Publisher: Springer