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FAN: Finding Accurate iNductions

delete2002-04-01
delete12
PRE
AI
J
José Ranilla
A
A. Bahamonde
DOI：10.1006/ijhc.2002.1002delete
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Abstract

Abstract

En 中文
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.
Keywords:
machine learning
classification rules
minimum distance
induction from examples
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Journal

I
International Journal of Human-Computer Studies
IF:
5.1
Papers:
2.8K
Citations:
8.9K

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