Titlebook: Computational Science – ICCS 2021; 21st International C Maciej Paszynski,Dieter Kranzlmüller,Peter M.A. Sl Conference proceedings 2021 Spri

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https://doi.org/10.1007/978-981-99-1191-2D) detection, is non-trivial, a number of methods to do this have been proposed. These methods are mostly heuristic, with no clear consensus in the literature as to which should be used in specific OoD detection tasks. In this work, we focus on a recently proposed, yet popular, Extreme Value Machine

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https://doi.org/10.1007/978-981-99-1191-2em, there is still a need to look for better ones, which can overcome the limitations of known methods. For this reason we developed a new algorithm that in contrast to traditional random undersampling removes maximum . nearest neighbors of the samples which belong to the majority class. In such a w

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https://doi.org/10.1007/978-981-99-1191-2client’s next purchase or the next location visited that focus on achieving the best possible prediction quality in terms of different quality metrics. Within such approaches, the quality is however usually evaluated on the entire set of clients, without dividing them into classes with a different p