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Intuitionistic Fuzzy Laplacian Twin Support Vector Machine for semi-supervised classification

Journal of the Operations Research Society of China 2022年第1期10卷 89-112页

作者： Jia-Bin Zhou Yan-Qin Bai Yan-Ru Guo Hai-Xiang Lin Department of Mathematics Shanghai UniversityShanghai 200444China Delft Institute of Applied Mathematics Delft University of TechnologyDelft 2600GAThe Netherlands

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 classi-fier 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.

关键词： Twin support vector machine semi-supervised classification Intuitionistic fuzzy Manifold regularization Noisy data

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semi-supervised RADIO TRANSMITTER classification BASED ON ELASTIC SPARSITY REGULARIZED SVM

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Journal of Electronics(China) 2012年第6期29卷 501-508页

作者： Hu Guyu Gong Yong Chen Yande Pan Zhisong Deng Zhantao Institute of Command Automation PLA University of Science and Technology Nanjing Army Command College Nanjing 210045

Non-collaborative radio transmitter recognition is a significant but challenging issue, since it is hard or costly to obtain labeled training data samples. In order to make effective use of the unlabeled samples which can be obtained much easier, a novel semi-supervised classification method named Elastic Sparsity Regularized Support Vector Machine (ESRSVM) is proposed for radio transmitter classification. ESRSVM first constructs an elastic-net graph over data samples to capture the robust and natural discriminating information and then incorporate the information into the manifold learning framework by an elastic sparsity regularization term. Experimental results on 10 GMSK modulated Automatic Identification System radios and 15 FM walkie-talkie radios show that ESRSVM achieves obviously better performance than KNN and SVM, which use only labeled samples for classification, and also outperforms semi-supervised classifier LapSVM based on manifold regularization.

关键词： Radio transmitter recognition Cyclic spectrum density semi-supervised classification Elastic Sparsity Regularized Support Vector Machine (ESRSVM)

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Pointwise manifold regularization for semi-supervised learning

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Frontiers of Computer Science 2021年第1期15卷 91-98页

作者： Yunyun WANG Jiao HAN Yating SHEN Hui XUE Department of Computer Science and Engineering Nanjing University of Posts&TelecommunicationsNanjing 210046China School of Computer Science and Engineering Southeast UniversityNanjing 210096China

Manifold regularization(MR)provides a powerful framework for semi-supervised classification using both the labeled and unlabeled data.It constrains that similar instances over the manifold graph should share similar classification out-puts according to the manifold assumption.It is easily noted that MR is built on the pairwise smoothness over the manifold graph,i.e.,the smoothness constraint is implemented over all instance pairs and actually considers each instance pair as a single operand.However,the smoothness can be pointwise in nature,that is,the smoothness shall inherently occur“everywhere"to relate the behavior of each point or instance to that of its close neighbors.Thus in this paper,we attempt to de-velop a pointwise MR(PW_MR for short)for semi-supervised learning through constraining on individual local instances.In this way,the pointwise nature of smoothness is preserved,and moreover,by considering individual instances rather than instance pairs,the importance or contribution of individual instances can be introduced.Such importance can be described by the confidence for correct prediction,or the local density,for example.PW.MR provides a different way for implementing manifold smoothness Finally,empirical results show the competitiveness of PW_MR compared to pairwise MR.

关键词： semi-supervised classification manifold regularization pairwise smoothness pointwise smoothness local density

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