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.
Bibliographical noteFunding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. 2010-0012631 ).
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Artificial Intelligence