Cost-sensitive decision tree and cost-sensitive naïve Bayes are both new cost-sensitive learning models proposed recently to minimize the total cost of test and misclassifications...
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...
In this paper we shall represent strategic planning problems by dynamic decision trees, in which the nodes are projects that can be deferred or postponed for a certain period of t...
Machine Learning algorithms can act as a valuable analytical tool in design research. In this paper, we demonstrate the application of a decision tree learning algorithm for desig...
Capacity control in perceptron decision trees is typically performed by controlling their size. We prove that other quantities can be as relevant to reduce their flexibility and co...
Kristin P. Bennett, Nello Cristianini, John Shawe-...