Empirical Evaluation of Ensemble Techniques for a Pittsburgh Learning Classifier System
نویسندگان
چکیده
Ensemble techniques have proved to be very useful to boost the performance of several types of machine learning methods. In this paper, we illustrate its usefulness in combination with GAssist, a Pittsburgh-style Learning Classifier System. Two types of ensemble are tested. First baggingstyle consensus prediction. Second an ensemble intended to deal more efficiently with ordinal classification problems. Both methods improve the performance and behaviour of GAssist in the tested domains.
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تاریخ انتشار 2007