This paper addresses the problem of the explanation of the result given by a decision tree, when it is used to predict the class of new cases. In order to evaluate this result, the...
This paper addresses the issue of the explanation of the result given to the end-user by a classifier, when it is used as a decision support system. We consider machine learning cl...
There has been little research into how end-user programming environments can provide explanations that could fill a critical information gap for end-user debuggers – help with ...
Kernel Miner is a new data-mining tool based on building the optimal decision forest. The tool won second place in the KDD'99 Classifier Learning Contest, August 1999. We des...
We propose a new decision tree algorithm, Class Confidence Proportion Decision Tree (CCPDT), which is robust and insensitive to class distribution and generates rules which are st...
Wei Liu, Sanjay Chawla, David A. Cieslak, Nitesh V...