代价敏感学习中的损失函数设计
Design of loss function for cost-sensitive learning
摘要点击 5848  全文点击 2576  投稿时间:2014-06-05  修订日期:2015-01-09
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DOI编号  10.7641/CTA.2015.40519
  2015,32(5):689-694
中文关键词  学习算法  代价敏感学习  损失函数  Bayes最优决策  代价敏感损失
英文关键词  learning algorithms  cost-sensitive learning  loss function  Bayes optimal decision  cost-sensitive risk
基金项目  国家自然科学青年基金项目(31200496, 61473156), 中国博士后基金项目(2014M551487), 江苏省博士后基金项目(1301009A)资助.
学科分类代码  
作者单位E-mail
李秋洁 南京林业大学 机械电子工程学院 liqiujie_1@163.com 
赵亚琴 南京林业大学 机械电子工程学院  
顾洲 南京林业大学 机械电子工程学院  
中文摘要
      一般的学习算法通过最小化分类损失使分类错误率最小化, 而代价敏感学习则以最小化分类代价为目标, 需构造代价敏感损失. 本文探讨代价敏感损失的设计准则, 首先介绍基于代价敏感风险优化的代价敏感学习方法, 然后在Bayes最优分类理论框架下, 提出两条代价敏感损失设计准则. 接着采用两种常用代价敏感损失生成方法构 造平方损失、指数损失、对数损失、支持向量机损失等经典损失函数的代价敏感扩展形式.根据所提出的设计准则, 从理论上分析这些代价敏感损失的性能. 最后通过实验表明, 同时满足两条设计准则的代价敏感损失能有效降低分 类代价, 从而证明了本文提出的代价敏感损失设计准则的合理性.
英文摘要
      Conventional learning algorithms minimize the classification error through minimizing the classification loss. However, the cost-sensitive learning minimizes the classification cost; thus, cost-sensitive losses have to be constructed. This paper studies the design criteria for cost-sensitive loss functions. Firstly, cost-sensitive learning methods based on cost-sensitive risk minimization are briefly introduced. Then, under the theory framework of Bayes optimal classification, two design guidelines of cost-sensitive loss function are proposed. The cost-sensitive extensions of several classic loss functions (e.g., square loss, exponential loss, log loss and support vector machine (SVM) loss) are generated via two most popular construction methods of cost-sensitive loss. The performances of these cost-sensitive losses are theoretically analyzed based on the proposed two design guidelines. Experimental results have shown that those cost-sensitive losses that satisfy both of the two design criteria significantly reduce classification costs, demonstrating the rationality of the proposed design criteria of cost-sensitive loss.