Fuzzy Locality Preserving Projection Twin Support Vector Machine for Classification

被引:0
作者
Zhao, Jie [1 ]
Wang, Lei [1 ]
Ji, Hongbing [1 ]
Chen, Shuangyue [1 ]
Li, Danping [2 ]
机构
[1] Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China
[2] Xidian Univ, Sch Telecommun Engn, Xian 710071, Peoples R China
来源
2017 CHINESE AUTOMATION CONGRESS (CAC) | 2017年
基金
中国国家自然科学基金;
关键词
pattern classification; projection twin support vector machine; fuzzy membership; local geometrical structure information; successive overrelaxation algorithm; MEMBERSHIP FUNCTION; SVM;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Recursive projection twin support vector machine (PTSVM) and Locality preserving projection twin support vector machine (LPPTSVM) are two extensions of traditional support vector machine (SVM). However, they may lead to a weak classifier in the application where some data points may not be fully assigned to one class. In this paper, we introduce the basic idea of fuzzy membership into LPPTSVM and propose a fuzzy LPPTSVM (FLPPTSVM) algorithm. Through fuzzy weighting technique, input points with different confidence degrees can make different contributions to the learning of decision surface. In this way, FLPPTSVM not only maintains the advantages of LPPTSVM which considers the local geometrical structure of the data and enhances the generalization ability of the algorithm, but also decreases the influence of outliers and noises. We construct experiments on artificial and UCI benchmark datasets and the results show the effectiveness and robustness of the proposed FLPPTSVM method.
引用
收藏
页码:5859 / 5864
页数:6
相关论文
共 24 条
[1]  
[Anonymous], 2000, ACTA AUTOM SIN
[2]  
[Anonymous], 2003, NIPS
[3]  
Anzai Y., 2012, PATTERN RECOGNITION
[4]   Locality preserving projection twin support vector machine and its application in classification [J].
Chen, Sugen ;
Wu, Xiaojun ;
Xu, Juan .
JOURNAL OF ALGORITHMS & COMPUTATIONAL TECHNOLOGY, 2016, 10 (02) :65-72
[5]   Recursive projection twin support vector machine via within-class variance minimization [J].
Chen, Xiaobo ;
Yang, Jian ;
Ye, Qiaolin ;
Liang, Jun .
PATTERN RECOGNITION, 2011, 44 (10-11) :2643-2655
[6]  
CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411
[7]   A relative evaluation of multiclass image classification by support vector machines [J].
Foody, GM ;
Mathur, A .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2004, 42 (06) :1335-1343
[8]  
GAO BB, 2015, MACH LEARN APPL ICML, P7, DOI DOI 10.1109/ICMLA.2015.35
[9]  
Han-Pang Huang, 2002, International Journal of Fuzzy Systems, V4, P826
[10]   Twin support vector machines for pattern classification [J].
Jayadeva ;
Khemchandani, R. ;
Chandra, Suresh .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2007, 29 (05) :905-910