Abstract
Multiple classification rules are simultaneously identified by applying the Cross-Entropy method to the maximization of accuracy measures in a supervised learning context. Optimal ensembles of rules are searched through stochastic traversals of the rule space. Each rule contributes to classify a given instance when the observed attribute values belong to specific subsets of the corresponding attribute domains. Classifications of the various rules are combined applying majority voting schemes. The performance of the proposed algorithm has been tested on some data sets from the UCI repository.
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Lafratta, G. (2016). Cross-Entropy Based Ensemble Classifiers. In: Bucciarelli, E., Silvestri, M., Rodríguez González, S. (eds) Decision Economics, In Commemoration of the Birth Centennial of Herbert A. Simon 1916-2016 (Nobel Prize in Economics 1978). Advances in Intelligent Systems and Computing, vol 475. Springer, Cham. https://doi.org/10.1007/978-3-319-40111-9_6
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DOI: https://doi.org/10.1007/978-3-319-40111-9_6
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