代价敏感Boosting算法研究  

Cost-sensitive boosting algorithms

作　　者：李秋洁[1] 茅耀斌[1] 叶曙光[1] 王执铨[1] 

机构地区：[1]南京理工大学自动化学院,江苏南京210094

出　　处：《南京理工大学学报》2013年第1期19-24,31,共7页Journal of Nanjing University of Science and Technology

基　　金：国家自然科学基金(60974129;70931002);国家科技重大专项(2011ZX04002-051);中央高校基本科研业务费专项资金资助项目(NUST2011YBZM119)

摘　　要：针对代价敏感学习问题,研究boosting算法的代价敏感扩展。提出一种基于代价敏感采样的代价敏感boosting学习方法,通过在原始boosting每轮迭代中引入代价敏感采样,最小化代价敏感损失期望。基于上述学习框架,推导出两种代价敏感boosting算法,同时,揭示并解释已有算法的不稳定本质。在加州大学欧文分校(University of California,Irvine,UCI)数据集和麻省理工学院生物和计算学习中心(Center for Biological&Computational Learning,CBCL)人脸数据集上的实验结果表明,对于代价敏感分类问题,代价敏感采样boosting算法优于原始boosting和已有代价敏感boosting算法。In terms of the problem of cost-sensitive learning,this paper investigates cost-sensitive extension of boosting.A cost-sensitive boosting learning framework is proposed based on cost-sensitive sampling.Through introducing cost-sensitive sampling in each round of naive boosting,the expectation of cost-sensitive loss is minimized.Under the above framework,two new cost-sensitive boosting algorithms are deduced.Meanwhile,issues of the instability existing in early cost-sensitive boosting algorithms are revealed and explained.Experimental results on UCI(University of California,Irvine)data set and CBCL(Center for Biological & Computational Learning)face data set demonstrate:in terms of the cost-sensitive classification problem,cost-sensitive sampling boosting algorithms outperform naive boosting and existing cost-sensitive boosting algorithms.

关 键 词：BOOSTING 代价敏感boosting 代价敏感学习 代价敏感采样 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]