Abstract A dynamic programming algorithm for constructing optimal dyadic decision trees was recently introduced, analyzed, and shown to be very effective for low dimensional data ...
This paper investigates how the splitting criteria and pruning methods of decision tree learning algorithms are influenced by misclassification costs or changes to the class distr...
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...
Abstract. Ensemble methods are able to improve the predictive performance of many base classifiers. Up till now, they have been applied to classifiers that predict a single target ...
Dragi Kocev, Celine Vens, Jan Struyf, Saso Dzerosk...
Abstract. We study predicate selection functions (also known as splitting rules) for structural decision trees and propose two improvements to existing schemes. The first is in cl...