PSO-Based Method for SVM Classification on Skewed Data-Sets

被引：6

作者：

Cervantes, Jair ^{[1
]}

Garcia-Lamont, Farid ^{[1
]}

Lopez, Asdrubal ^{[3
]}

Rodriguez, Lisbeth ^{[2
]}

Ruiz Castilla, Jose S. ^{[1
]}

Trueba, Adrian ^{[1
]}

机构：

[1] Autonomous Univ Mexico State, Posgrad & Invest UAEMEX, Fracc El Tejocote 56259, Texcoco, Mexico

[2] Inst Tecnol Orizaba, Div Res & Postgrad Studies, Orizaba, Veracruz, Mexico

[3] Univ State Mexico, Zumpango Univ Ctr, Texcoco, Mexico

来源：

ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS, ICIC 2015, PT III | 2015年 / 9227卷

关键词：

Support vector machines; PSO; Imbalanced data sets; NEURAL-NETWORKS;

D O I：

10.1007/978-3-319-22053-6_9

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Support Vector Machines (SVM) have shown excellent generalization power in classification problems. However, on skewed data-sets, SVM learns a biased model that affects the classifier performance, which is severely damaged when the unbalanced ratio is very large. In this paper, a new external balancing method for applying SVM on skewed data sets is developed. In the first phase of the method, the separating hyperplane is computed. Support vectors are then used to generate the initial population of PSO algorithm, which is used to improve the population of artificial instances and to eliminate noise instances. Experimental results demonstrate the ability of the proposed method to improve the performance of SVM on imbalanced data-sets.

引用

页码：79 / 86

页数：8

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