A weighted one-class support vector machine

被引：70

作者：

Zhu, Fa ^{[1
]}

Yang, Jian ^{[1
]}

Gao, Cong ^{[2
]}

Xu, Sheng ^{[3
]}

Ye, Ning ^{[4
]}

Yin, Tongming ^{[5
]}

机构：

[1] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing, Jiangsu, Peoples R China

[2] Univ Regina, Dept Comp Sci, Regina, SK S4S 0A2, Canada

[3] Univ Calgary, Dept Geomat Engn, Calgary, AB T2N 1N4, Canada

[4] Nanjing Forestry Univ, Coll Informat Sci & Technol, Nanjing, Jiangsu, Peoples R China

[5] Nanjing Forestry Univ, Coll Forest Resources & Environm, Nanjing, Jiangsu, Peoples R China

来源：

NEUROCOMPUTING | 2016年 / 189卷

关键词：

Weighted one-class support vector machine; One-class classification; Neighbors' distribution knowledge; Instance weights; ONE-CLASS CLASSIFICATION; ALGORITHM;

D O I：

10.1016/j.neucom.2015.10.097

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The standard one-class support vector machine (OC-SVM) is sensitive to noises, since every instance is equally treated. To address this problem, the weighted one-class support vector machine (WOC-SVM) was presented. WOC-SVM weakens the impact of noises by assigning lower weights. In this paper, a novel instance-weighted strategy is proposed for WOC-SVM. The weight is only relevant to the neighbors' distribution knowledge, which is only decided by k-nearest neighbors. The closer to the boundary of the data distribution the instance is, the lower the corresponding weight is. The experimental results demonstrate that WOC-SVM outperforms the standard OC-SVM when using the proposed instance weighted strategy. The proposed instance-weighted method performs better than previous ones. (C) 2015 Elsevier B.V. All rights reserved.

引用

页码：1 / 10

页数：10

共 38 条

[1] KEEL: a software tool to assess evolutionary algorithms for data mining problems [J].

Alcala-Fdez, J. ;

Sanchez, L. ;

Garcia, S. ;

del Jesus, M. J. ;

Ventura, S. ;

Garrell, J. M. ;

Otero, J. ;

Romero, C. ;

Bacardit, J. ;

Rivas, V. M. ;

Fernandez, J. C. ;

Herrera, F. .

SOFT COMPUTING, 2009, 13 (03) :307-318

[2]

Alcalá-Fdez J, 2011, J MULT-VALUED LOG S, V17, P255

[3]

Bache K., 2013, UCI Machine Learning Repository

[4] Soft clustering using weighted one-class support vector machines [J].

Bicego, Manuele ;

Figueiredo, Mario A. T. .

PATTERN RECOGNITION, 2009, 42 (01) :27-32

[5]

Boser B. E., 1992, Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, P144, DOI 10.1145/130385.130401

[6] The use of the area under the roc curve in the evaluation of machine learning algorithms [J].

Bradley, AP .

PATTERN RECOGNITION, 1997, 30 (07) :1145-1159

[7]

Cabral GG, 2014, IEEE SYS MAN CYBERN, P2551, DOI 10.1109/SMC.2014.6974311

[8] LIBSVM: A Library for Support Vector Machines [J].

Chang, Chih-Chung ;

Lin, Chih-Jen .

ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2011, 2 (03)

[9]

Cherkassky V, 1997, IEEE Trans Neural Netw, V8, P1564, DOI 10.1109/TNN.1997.641482

[10] Least squares one-class support vector machine [J].

Choi, Young-Sik .

PATTERN RECOGNITION LETTERS, 2009, 30 (13) :1236-1240

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