Decision Tree Induction Method Research Articles

The Accuracy of Decision Tree Induction in a Noisy Domain for Expert Systems Construction

AbstractMany studies have shown that decision tree induction methods could be used to determine rules for expert systems. Pruning techniques are often used to increase the accuracy of an induced decision tree over the instance space. While recent results of decision tree induction show that large samples may be required to induce a decision tree of small error, recent expository studies have used very small sample sizes. In such cases it is of value to obtain a posterior evaluation of the error of the induced concept. In this paper we give three methods to estimate the accuracy of a pruned decision tree. The first method assumes uniform prior distribution. For those cases where uniform prior is not appropriate, we develop a method to obtain appropriate prior using a beta distribution. Finally, we provide a general bound which requires no assumption over the instance space. These results can be used when a pruned decision tree is used to classify the original domain or another close domain.

An Empirical Comparison of Pruning Methods for Decision Tree Induction

This paper compares five methods for pruning decision trees, developed from sets of examples. When used with uncertain rather than deterministic data, decision-tree induction involves three main stages—creating a complete tree able to classify all the training examples, pruning this tree to give statistical reliability, and processing the pruned tree to improve understandability. This paper concerns the second stage—pruning. It presents empirical comparisons of the five methods across several domains. The results show that three methods—critical value, error complexity and reduced error—perform well, while the other two may cause problems. They also show that there is no significant interaction between the creation and pruning methods.