Relative Unsupervised Discretization for Regresseion Problems

The paper describes a new, context-sensitive discretization algorithm that combines aspects of unsupervised (class-blind) and supervised methods. The algorithm is applicable to a wide range of machine learning and data mining problems where continuous attributes need to be discretized. In this paper, we evaluate its utility in a regression-by-classification setting. Preliminary experimental results indicate that the decision trees induced using this discretization strategy are significantly smaller and thus more comprehensible than those learned with standard discretization methods, while losing only minimally in numerical prediction accuracy. This may be a considerable advantage in machine learning and data mining applications where comprehensibility is an issue.