Shuxia Lu

Shenzhen University

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Xizhao Wang

Shenzhen University

Xu Zhou

Shanghai University

Junhai Zhai

Hebei University

Xianhao Liu

Shenzhen University Health Science Center

Meng Zhang

National Astronomical Observatories

G. M. Cao

Northeast Petroleum University

J. Meng

Peking University

Lianxiang Cai

Xiyuan Hospital

Guiqiang Zhang

Xingyi Normal University for Nationalities

Lisha Hu

Hebei University of Economics and Business

Cooperative Institutions

Hebei University

University of Chinese Academy of Sciences

Chinese Academy of Sciences

Beijing University of Posts and Telecommunications

Shenzhen University

Peking University

Hohai University

Chongqing University

Huazhong University of Science and Technology

Shenzhen University Health Science Center

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Compact Fuzzy Multiclass Support Vector Machines

Shuxia Lu Shi Pu Xianhao Liu

The fuzzy support vector machines (FSVMs) can be used to deal with multiclass classification problems where the key issue is to solve a quadratic programming problem. This paper introduces a new fuzzy multiclass support vector machines (FMSVMs) based on compact description of data, which extends the exiting support vector machine method to the case of k-class problem in one optimization task (quadratic programming) by considering the relative location of samples to the origin and the knowledge of ambiguity associated with the membership of samples for a given class. The fuzzy membership is defined by not only the relation between a sample and its cluster center, but also by the affinity among samples. Compared with the existing SVMs, our new proposed FMSVMs have the improvement in aspects of classification accuracy and reducing the effects of noises and outliers.

Multiclass classification

10.1109/icnc.2008.832

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FAST FUZZY MULTICATEGORY SVM BASED ON SUPPORT VECTOR DOMAIN DESCRIPTION

International Journal of Pattern Recognition and Artificial Intelligence (2008)

Xizhao Wang Shuxia Lu Junhai Zhai

This paper proposes a fast fuzzy classifier of multicategory support vector machines (FMSVM) based on support vector domain description (SVDD). The main idea is that the proposed FMSVM is obtained by directly considering all data in one optimization formulation, using a fuzzy membership to each input point. The fuzzy membership is determined by support vector domain description (SVDD). For making support vector machine (SVM) more practical, we use an implement of the modified sequential minimal optimization (SMO) that can quickly solve SVM quadratic programming (QP) problems without any extra matrix storage or the use of numerical QP optimization steps at all. Compared with the existing SVMs, the newly proposed FMSVM that uses the L 2 -norm in the objective function shows improvement with regards to accuracy of classification and reduction of the effects of noises and outliers. The experiment also shows the efficiency of the modified SMO for expediting the training of SVM.

Relevance vector machine

Ranking SVM

Margin classifier

10.1142/s0218001408006144

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Citations (51)

基于零阶减小方差方法的鲁棒支持向量机 (Robust SVM Based on Zeroth Order Variance Reduction).

计算机科学 (2019)

Shuxia Lu Lianxiang Cai Zhang Luohuan

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Density Weighted Proximal Support Vector Machine

Computer Science (2012)

Shuxia Lu

The regularized least-squares problem replaces the quadratic programming problem in the standard proximal support vector machines(PSVM).The proximal support vector machines has an analytic solution,so it reduces the training time.But the unbalanced data of positive and negative class is disregarded in the standard proximal support vector machines,and the same penalty factors are assigned to the all training samples.In practical problem,the distribution of positive and negative class is unbalance.Aiming at this problem,a density weighted proximal support vector machines(DPSVM) based on the support vector machines was presented,it is a modified proximal support vector machines algorithm.First calculated the density information of the different data,then according to the density information of the different sample,the different penalty factors were assigned to different training sample which has different density.The penalty values in the original problem of proximal support vector machines were transformed into a diagonal matrix.This method was used on UCI dataset,and compared with support vector machines and proximal support vector Machines methods.The experiment results indicate that the density weighted proximal support vector machines have a better efficiency classified performance in the unbalance data sets of positive and negative samples.

Relevance vector machine

Sample (material)

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A comparison among four SVM classification methods: LSVM, NLSVM, SSVM and NSVM

Shuxia Lu Xizhao Wang

Support vector machines (SVMs) are powerful tools for providing solutions to classification and function approximation problems. The comparison among the four classification methods is conducted. The four methods are Lagrangian support vector machine (LSVM), finite Newton Lagrangian support vector machine (NLSVM), smooth support vector machine (SSVM) and finite Newton support vector machine (NSVM). The comparison of their algorithm in generating a linear or nonlinear kernel classifier, accuracy and computational complexity is also given. The study provides some guidelines for choosing an appropriate one from four SVM classification methods in a classification problem.

Relevance vector machine

Linear classifier

Kernel (algebra)

Margin classifier

Statistical classification

10.1109/icmlc.2004.1384589

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Citations (25)

Learning the Parameters for Least Squares Support

Shuxia Lu Xiaoxue Fan

the regularization parameter and kernel parameter play important roles in the performance of the least squares support vector machine (LS-SVM). Aimed at optimizing the LS- SVM's parameters, a fast method based on distance is presented. The method is by way of calculating the various types of distances in the feature space to determine the optimal kernel parameter. Since the method only needs to calculate some simple mathematical formulas, and avoids training the corresponding LS-SVM classifiers, the method can greatly reduce the training time. Experiment results show that the proposed method can improve the training speed.

Kernel (algebra)

Regularization

Least-squares function approximation

Feature vector

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Induction of multiple fuzzy decision trees based on rough set technique

Information Sciences (2008)

Xizhao Wang Junhai Zhai Shuxia Lu

Reduct

Defuzzification

Rule induction

10.1016/j.ins.2008.03.021

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Citations (163)

A new and informative active learning approach for support vector machine

Information Sciences (2013)

Lisha Hu Shuxia Lu Xizhao Wang

Hyperplane

Online machine learning

Labeled data

Relevance vector machine

10.1016/j.ins.2013.05.010

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Citations (24)

Support Vector Machine for Suppressing Error Method

Shuxia Lu Pu Shi Ming Chen

Support Vector Machine (SVM) is sensitive to noises and outliers. For reducing the effect of noises and outliers, we propose a novel SVM for suppressing error function. The error function is limited to the interval of. The separation hypersurface is simplified and the margin of hypersurface is widened. Experimental results show that our proposed method is able to simultaneously increase the classification efficiency and the generalization ability of the SVM.

Hypersurface

Margin (machine learning)

Error function

10.1109/icise.2009.1149

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A weighted least squares support vector machine based on covariance matrix

Shuxia Lu Runa Tian Yufen Zhang

In this paper, a weighted least squares support vector machine based on covariance matrix (CWLSSVM) is proposed. The structural information is vital for designing a good classifier in real-world problem, so the proposed method adds the covariance matrix of data to the objective function to identify the structure information in data. The LSSVM is sensitive to outliers, so a new weighting method is proposed according to distances between different types of samples and the center of samples. Different weights are assigned to different training samples in the error term of the objective function. Experimental results show that the CWLSSVM outperforms the LSSVM, the SVM and the ESVM.

Matrix (chemical analysis)

10.1109/icwapr.2015.7295949

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Citations (1)