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

Jia Bin Zhou, Yan Qin Bai*, Yan Ru Guo, Hai Xiang Lin

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review


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.

Original languageEnglish
Number of pages24
JournalJournal of the Operations Research Society of China
Publication statusPublished - 2021

Bibliographical note

The original version of this article unfortunately contained numerous spellingmistakes on its second and third page. These were caused entirely by a technical defect on our side that sadly remained undetected. The original article was corrected.
The publisher apologises for this mistake.


  • Intuitionistic fuzzy
  • Manifold regularization
  • Noisy data
  • Semi-supervised classification
  • Twin support vector machine


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