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首页> 外文会议>Advances in Knowledge Acquisition and Management; Lecture Notes in Artificial Intelligence; 4303 >Training Classifiers for Unbalanced Distribution and Cost-Sensitive Domains with ROC Analysis
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Training Classifiers for Unbalanced Distribution and Cost-Sensitive Domains with ROC Analysis

机译：具有ROC分析的不平衡分销和成本敏感领域的培训分类器

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摘要

ROC (Receiver Operating Characteristic) has been used as a tool for the analysis and evaluation of two-class classifiers, even the training data embraces unbalanced class distribution and cost-sensitiveness. However, ROC has not been effectively extended to evaluate multi-class classifiers. In this paper, we proposed an effective way to deal with multi-class learning with ROC analysis. An EMAUC algorithm is implemented to transform a multi-class training set into several two-class training sets. Classification is carried out with these two-class training sets. Empirical results demonstrate that the classifiers trained with the proposed algorithm have competitive performance for unbalanced distribution and cost-sensitive domains.
机译：ROC（接收器工作特性）已被用作分析和评估两类分类器的工具，即使培训数据也包含了不平衡的类分布和成本敏感性。但是，ROC尚未有效扩展到评估多类分类器。在本文中，我们提出了一种利用ROC分析处理多类别学习的有效方法。实施EMAUC算法可将多类训练集转换为几个两类训练集。使用这两个类的训练集进行分类。实验结果表明，采用该算法训练的分类器在不平衡分布和成本敏感域方面具有竞争优势。

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