Improving Decision Tree Performance Through Induction- and Cluster-Based Stratified Sampling

Abstract

It is generally recognised that recursive partitioning, as used in the construction of classification trees, is inherently unstable, particularly for small data sets. Classification accuracy and, by implication, tree structure, are sensitive to changes in the training data. Successful approaches to counteract this effect include multiple classifiers, e.g. boosting, bagging or windowing. The downside of these multiple classification models, however, is the plethora of trees that result, often making it difficult to extract the classifier in a meaningful manner. We show that, by using some very weak knowledge in the sampling stage, when the data set is partitioned into the training and test sets, a more consistent and improved performance is achieved by a single decision tree classifier.