Integrative Windowing
<|reference_start|>Integrative Windowing: 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.<|reference_end|>
@article{fürnkranz1998integrative,
    title={Integrative Windowing},
    author={J. F"urnkranz},
    journal={Journal of Artificial Intelligence Research, Vol 8, (1998),
  129-164},
    year={1998},
    doi={10.1613/jair.487},
    archivePrefix={arXiv},
    eprint={cs/9805101},
    primaryClass={cs.AI}
}