Induction of condensed determinations
Pat Langley · 1996
In this paper we suggest determinations as a representation of knowledge that should be easy to understand. We briefly review determinations, which can be displayed in a tabular format, and their use in prediction, which involves a simple matching process. We describe ConDet, an algorithm that uses feature selection to construct determinations from training data, augmented by a condensation process that collapses rows to produce simpler structures. We report experiments that show condensation reduces complexity with no loss of accuracy, then discuss ConDet's relation to other work and outline directions for future studies. Introduction Understandability is a major concern in knowledge discovery and data mining. Although it is important to discover knowledge that is accurate, in many domains it is also essential that users find that knowledge easy to interpret. Most researchers assume that logical rules and decision trees are more understandable than other formalisms, such as neural n...