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 |
本文献已被 ScienceDirect 等数据库收录! |
|