为了使决策树健壮，我们从描述信息增益开始，关于这个规则的置信度，使用C4.5作为度量。这可以使我们快速的解释为什么信息增益，象置信度，偏重大多数类的规则的结果。为了克服这种偏见，我们介绍一种新度量，类置信度比例（CCP），它是CCPDT（类置信度比例决策树）形成的基础。这两种变化在一起产生一个分类器，它不仅比传统的决策树，而且比著名的平衡取样技术学习树能更好的完成统计。%In order to make decision trees robust, we begin by expressing Information Gain, the metric used in C4.5, in terms of con- fidence of a rule. This allows us to immediately explain why Information Gain. like confidence, results in rules which are biased towards the majority class. To overcome this bias. we introduce a new measure. Class Confidence Proportion （CCP）, which forms the basis of CCPDT. Together these two changes yield a classifier that performs statistically better than not only traditional decision trees but also trees learned from data that has been balanced by well known sampling techniques.
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