Abstract：Almost all the available algorithms deal with the imbalanced problems by directly weighting the loss functions. In this paper, a loss by weighting the margin in hinge function is proposed and its Bayesian consistency is proved. Furthermore, a learning algorithm, called Weighting Margin SVM (WMSVM), is obtained and SMO can be modified to solve WMSVM. Experimental results on certain benchmark datasets demonstrate the effectiveness of WMSVM. Both of the theoretical and experimental analysis indicate that the proposed weighted margin loss function method enriches the cost-sensitive learning.
陶卿，梁万路，孔康，汪群山. 加权边缘损失函数的代价敏感支持向量机[J]. 模式识别与人工智能, 2011, 24(6): 763-768.
TAO Qing, LIANG Wan-Lu, KONG Kang, Wang Qun-Shan. Cost-Sensitive SVM Based on Loss Functions with Weighted Margin. , 2011, 24(6): 763-768.
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