Abstract

In this technical report a novel method is proposed that extends the decision tree framework, allowing standard decision tree classifiers to provide a unique certainty value for every input sample they classify. This value is calculated for every input sample individually and represents the classifier's certainty in the classification. The algorithm proposed in this report is not limited to axis-parallel trees, it can be applied to any kind of decision tree where the decisions are

Citations