期刊
PATTERN RECOGNITION
卷 47, 期 9, 页码 3158-3167出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2014.03.008
关键词
Imbalanced data classification; Twin support vector machine; Weighted twin support vector machine; Lagrangian functions; Quadratic cost functions
资金
- National Natural Science Foundation of China [11201426, 11371365, 10971223]
- Zhejiang Provincial Natural Science Foundation of China [LQ12A01020, LQ13F030010]
- Ministry of Education, Humanities and Social Sciences Research Project of China [13YJC910011]
In this paper, we propose an efficient weighted Lagrangian twin support vector machine (WLTSVM) for the imbalanced data classification based on using different training points for constructing the two proximal hyperplanes. The main contributions of our WLTSVM are: (1) a graph based under-sampling strategy is introduced to keep the proximity information, which is robustness to outliers, (2) the weight biases are embedded in the Lagrangian TWSVM formulations, which overcomes the bias phenomenon in the original TWSVM for the imbalanced data classification, (3) the convergence of the training procedure of Lagrangian functions is proven and (4) it is tested and compared with some other TWSVMs on synthetic and real datasets to show its feasibility and efficiency for the imbalanced data classification. (C) 2014 Elsevier Ltd. All rights reserved.
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