摘要
已有的非平衡数据分类算法主要采取直接对损失函数进行加权的方法.文中提出一种加权边缘的hinge损失函数并证明它的贝叶斯一致性,得到加权边缘支持向量机算法(WMSVM),并给出类似于SMO的求解方法.实验结果表明WMSVM在一些数据库上是有效的,从而从理论和实验上说明基于加权边缘的损失函数方法是已有代价敏感方法的一种较好补充.
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.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2011年第6期763-768,共6页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金资助项目(No.60835002
60975040)
关键词
非平衡数据问题
代价敏感分类
加权边缘
支持向量机
贝叶斯一致性
Imbalanced Data Problem, Cost-Sensitive Classification, Weighted Margin, SupportVector Machine, Bayesian Consistency
