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

Authors:
Jae Pil Hwang;Seongkeun Park;Euntai Kim
Affiliations:
School of Electrical and Electronic Engineering, Yonsei University, Sinchon-dong, Seodaemun-gu, Seoul 120-749, Republic of Korea;School of Electrical and Electronic Engineering, Yonsei University, Sinchon-dong, Seodaemun-gu, Seoul 120-749, Republic of Korea;School of Electrical and Electronic Engineering, Yonsei University, Sinchon-dong, Seodaemun-gu, Seoul 120-749, Republic of Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

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.