Flexible Decision Trees in a General Data-Mining Environment

J. W. Comley, L. Allison and L. J. Fitzgibbon, Fourth International Conference on Intelligent Data Engineering and Automated Learning (IDEAL-2003), Hong Kong, doi:10.1007/978-3-540-45080-1_72, 21-23 March 2003

Abstract. We describe a new data-mining platform, CDMS, aimed at the streamlined development, comparison and application of machine learning tools. We discuss its type system, focussing on the treatment of statistical models as first-class values.

This allows rapid construction of composite models - complex models built from simpler ones - such as mixture models, Bayesian networks and decision trees. We illustrate this with a flexible decision tree tool for CDMS which rather than being limited to discrete target attributes, can model any kind of data using arbitrary probability distributions.