摘要:分类是机器学习的重要任务之一.传统的分类学习算法追求最低的分类错误率，假设不同类型的错误分类具有相等的损失.然而，在诸如人脸识别门禁系统、软件缺陷预测、多标记学习等应用领域中，不同类型的错误分类所导致的损失差异较大.这要求学习算法对可能导致高错分损失的样本加以重点关注，使得学习模型的整体错分损失最小.为解决该问题，代价敏感学习方法引起了研究者的极大关注.以代价敏感学习方法的理论基础作为切入点，系统阐述了代价敏感学习的主要模型方法以及代表性的应用领域.最后，讨论并展望了未来可能的研究趋势.



Abstract:Classification is one of the most important tasks in machine learning. Conventional classification methods aim to attain low recognition error rate and assume the same loss from different kinds of misclassifications. However, in the applications such as the doorlocker system based on face recognition, software defect prediction and multi-label learning, different kinds of misclassification will lead to different losses. This requires the learning methods to pay more attention to the samples with high-cost misclassification, and thus make the total misclassification losses minimized. To deal with this issue, cost-sensitive learning has received the considerable attention from the researchers. This study takes the theoretical foundation of cost-sensitive learning as the focal point to analyze and survey its main models and the typical applications. At last, the difficulty and probable development trend of cost-sensitive learning are discussed.



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