FAN: Finding Accurate iNductions
In this paper we present a machine-learning algorithm that computes a small set of accurate and interpretable rules. The decisions of these rules can be straight-forwardly explained as the conclusions drawn by a case-based reasoner. Our system is named FAN, an acronym for finding accurate inductions. It starts from a collection of training examples and produces propositional rules able to classify unseen cases following a minimum-distance criterion in their evaluation procedure. In this way, we combine the advantages of instance-based algorithms and the conciseness of rule (or decision-tree) inducers, The algorithm followed by FAN can be seen as the result of successive steps of pruning heuristics. The main tool employed is that of the impurity level, a measure of the classification quality of a rule, inspired by a similar measure used in IB3. Finally, a number of experiments were conducted with standard benchmark datasets of the UCI repository to test the performance of our system, successfully comparing FAN with a wide collection of machine-learning algorithms. (C) 2002 Elsevier Science Ltd. All rights reserved.
machine learning classification rules minimum distance induction from examples