Abstract
In this paper new algorithm called Bayes metaclassifier (BMC) will be introduced as a method for improving weak classifiers performance. In general, BMC constitutes the probabilistic generalization of any base classifier and has the form of the Bayes scheme. To validate BMC classification two experiments were designed. In the first one three synthetic datasets were generated from normal distribution to calculate and check empirically upper bound for improving base classifier when BMC approach is applied. Furthermore, to validate usefulness of this algorithm extensive simulations from 22 available benchmarks were performed comparing BMC model against 8 base classifiers with different design paradigms.
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This work was supported by the statutory funds of the Department of Systems and Computer Networks, Wroclaw University of Science and Technology.
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Majak, M., Kurzyński, M. (2018). On a New Method for Improving Weak Classifiers Using Bayes Metaclassifier. In: Kurzynski, M., Wozniak, M., Burduk, R. (eds) Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. CORES 2017. Advances in Intelligent Systems and Computing, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-319-59162-9_27
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DOI: https://doi.org/10.1007/978-3-319-59162-9_27
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