Most decision tree induction methods used for extracting knowledge in classification problems are unable to deal with uncertainties embedded within the data, associated with human...
The belief decision tree (BDT) approach is a decision tree in an uncertain environment where the uncertainty is represented through the Transferable Belief Model (TBM), one interp...
A learner's performance does not rely only on the representation language and on the algorithm inducing a hypothesis in this language. Also the way the induced hypothesis is ...
In this paper we present an improvement of the precision of classification algorithm results. Two various approaches are known: bagging and boosting. This paper describes a set of ...
This paper extends previous work on the Skewing algorithm, a promising approach that allows greedy decision tree induction algorithms to handle problematic functions such as parit...