Intuitionistic Fuzzy Laplacian Twin Support Vector Machine for Semi-supervised Classification

Journal Article (2021)
Author(s)

Jia Bin Zhou (Shanghai University)

Yan Qin Bai (Shanghai University)

Yan Guo (Shanghai University)

Hai Xiang Lin (TU Delft - Mathematical Physics, TU Delft - Delft Institute of Applied Mathematics)

Research Group
Mathematical Physics
Copyright
© 2021 Jia Bin Zhou, Yan Qin Bai, Y. Guo, H.X. Lin
DOI related publication
https://doi.org/10.1007/s40305-021-00354-9
More Info
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Publication Year
2021
Language
English
Copyright
© 2021 Jia Bin Zhou, Yan Qin Bai, Y. Guo, H.X. Lin
Related content
Research Group
Mathematical Physics
Issue number
1
Volume number
10
Pages (from-to)
89-112
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Abstract

In general, data contain noises which come from faulty instruments, flawed measurements or faulty communication. Learning with data in the context of classification or regression is inevitably affected by noises in the data. In order to remove or greatly reduce the impact of noises, we introduce the ideas of fuzzy membership functions and the Laplacian twin support vector machine (Lap-TSVM). A formulation of the linear intuitionistic fuzzy Laplacian twin support vector machine (IFLap-TSVM) is presented. Moreover, we extend the linear IFLap-TSVM to the nonlinear case by kernel function. The proposed IFLap-TSVM resolves the negative impact of noises and outliers by using fuzzy membership functions and is a more accurate reasonable classifier by using the geometric distribution information of labeled data and unlabeled data based on manifold regularization. Experiments with constructed artificial datasets, several UCI benchmark datasets and MNIST dataset show that the IFLap-TSVM has better classification accuracy than other state-of-the-art twin support vector machine (TSVM), intuitionistic fuzzy twin support vector machine (IFTSVM) and Lap-TSVM.

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