Support Vector Machine Classifier with WHM Offset for Unbalanced Data
Boyang Li, Jinglu Hu, Kotaro Hirasawa · Journal of Advanced Computational Intelligence and Intelligent Informatics · 2008
We propose an improved support vector machine (SVM) classifier by introducing a new offset, for solving the real-world unbalanced classification problem. The new offset is calculated based on the unbalanced support vectors resulting from the unbalanced training data. We developed a weighted harmonic mean (WHM) algorithm to further reduce the effects of noise on offset calculation. We apply the proposed approach to classify real-world data. Results of simulation demonstrate the effectiveness of our proposed approach.