B. Li, J. Hu, and K. Hirasawa (Japan)
SVM, Unbalanced Data, Real-world Data Classification, Soft Decision-making Boundary
This paper proposes an improved support vector machine (SVM) classifier by introducing a soft decision-making boundary for solving real-world classification problem. The soft decision-making boundary contains two param eters describing the offset and the shape, which are esti mated automatically from the distribution of training sam ples around the boundary via a distribution of belief degree in the decision value domain. The SVM with soft decision making boundary increases classification accuracy by re ducing the effects of data unbalance and noises in the real world data. Simulation results show the effectiveness of the proposed approach.
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