A new weighted approach to imbalanced data classification problem via support vector machine with quadratic cost function

被引：67

作者：

Hwang, Jae Pil ^{[1
]}

Park, Seongkeun ^{[1
]}

Kim, Euntai ^{[1
]}

机构：

[1] Yonsei Univ, Sch Elect & Elect Engn, Seoul 120749, South Korea

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2011年 / 38卷 / 07期

关键词：

Imbalance dataset; Support vector machine; Lagrangian support vector machine; SVM; SMOTE;

D O I：

10.1016/j.eswa.2011.01.061

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a new weighted approach on Lagrangian support vector machine for imbalanced data classification problem is proposed. The weight parameters are embedded in the Lagrangian SVM formulation. The training method for weighted Lagrangian SVM is presented and its convergence is proven. The weighted Lagrangian SVM classifier is tested and compared with some other SVMs using synthetic and real data to show its effectiveness and feasibility. (C) 2011 Elsevier Ltd. All rights reserved.

引用

页码：8580 / 8585

页数：6

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