Journal of Artificial Intelligence Research 8 (1998), pp. 129-164. Submitted 11/97; published 5/98
© 1998 AI Access Foundation and Morgan Kaufmann Publishers. All rights reserved.
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Integrative Windowing

Johannes Fürnkranz
Carnegie Mellon University
School of Computer Science
Pittsburgh, PA 15213


In this paper we re-investigate windowing for rule learning algorithms. We show that, contrary to previous results for decision tree learning, windowing can in fact achieve significant run-time gains in noise-free domains and explain the different behavior of rule learning algorithms by the fact that they learn each rule independently. The main contribution of this paper is integrative windowing, a new type of algorithm that further exploits this property by integrating good rules into the final theory right after they have been discovered. Thus it avoids re-learning these rules in subsequent iterations of the windowing process. Experimental evidence in a variety of noise-free domains shows that integrative windowing can in fact achieve substantial run-time gains. Furthermore, we discuss the problem of noise in windowing and present an algorithm that is able to achieve run-time gains in a set of experiments in a simple domain with artificial noise.


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Next: Introduction