Decision trees are among the most effective and interpretable classification algorithms while ensembles techniques have been proven to alleviate problems regarding over-fitting and...
The current evaluation functions for heuristic planning are expensive to compute. In numerous domains these functions give good guidance on the solution, so it worths the computat...
Mostexisting decision tree systemsuse a greedyapproachto inducetrees -- locally optimalsplits are inducedat every node of the tree. Althoughthe greedy approachis suboptimal,it is ...
Dynamic evaluation is a technique for producing multiple results according to a decision tree which evolves with program execution. Sometimes it is desired to produce results for ...
A new approach to the induction of multivariate decision trees is proposed. A linear decision function (hyper-plane) is used at each non-terminal node of a binary tree for splittin...
Many organizations today have more than very large databases; they have databases that grow without limit at a rate of several million records per day. Mining these continuous dat...
Decision trees have been successfully used for the task of classification. However, state-of-the-art algorithms do not incorporate the user in the tree construction process. This ...
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. Ensemble methods are popular learning methods that usually increase the predictive accuracy of a classifier though at the cost of interpretability and insight in the deci...
: Decision Support Systems are proliferating rapidly in many areas of human endeavour including clinical medicine and psychology. While these are typically based on rulebased syste...