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An efficient weighted Lagrangian twin support vector machine for imbalanced data classification
Authors:Yuan-Hai Shao  Wei-Jie Chen  Jing-Jing Zhang  Zhen Wang  Nai-Yang Deng
Affiliation:1. Zhijiang College, Zhejiang University of Technology, Hangzhou 310024, PR China;2. College of Science China Agricultural University, Beijing 100083, PR China;3. College of Mathematics, Jilin University, Changchun 130012, PR China
Abstract: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.
Keywords:Imbalanced data classification  Twin support vector machine  Weighted twin support vector machine  Lagrangian functions  Quadratic cost functions
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