Abstract
Windowing methods are useful techniques to reduce the computational cost of Pittsburgh-style genetic-based machine learning techniques. If used properly, they additionally can be used to improve the classification accuracy of the system. In this paper we develop a theoretical framework for a windowing scheme called ILAS, developed previously by the authors. The framework allows us to approximate the degree of windowing we can apply to a given dataset as well as the gain in run-time. The framework sets the first stage for the development of a larger methodology with several types of learning strategies in which we can apply ILAS, such as maximizing the learning performance of the system, or achieving the maximum run-time reduction without significant accuracy loss.
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Bacardit, J., Goldberg, D.E., Butz, M.V., Llorà, X., Garrell, J.M. (2004). Speeding-Up Pittsburgh Learning Classifier Systems: Modeling Time and Accuracy. In: Yao, X., et al. Parallel Problem Solving from Nature - PPSN VIII. PPSN 2004. Lecture Notes in Computer Science, vol 3242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30217-9_103
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DOI: https://doi.org/10.1007/978-3-540-30217-9_103
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