A new weighted approach to imbalanced data classification problem via support vector machine with quadratic cost function |
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| Authors: | Jae Pil Hwang Seongkeun Park Euntai Kim |
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| Affiliation: | 1. College of Electrical and Information Engineering, Hunan University, Changsha 410082, China;2. Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Department of Computer Science, Minjiang University, Fuzhou 350121, China |
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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. |
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