Titlebook: Evolutionary Decision Trees in Large-Scale Data Mining; Marek Kretowski Book 2019 Springer Nature Switzerland AG 2019 Evolutionary Computa

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Parallel Computations for Evolutionary Inductionithms are available in every data mining commercial system, and they can be very easily applied without any profound awareness of the parameter settings or running details. Moreover, knowledge of the existence of alternative induction methods is still limited to a narrow group of researchers who are working on this topic.

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Global Induction of Univariate Treesdreau, Potvin in Handbook of metaheuristics. Springer, Berlin, 2010) [.]. However, their success in solving complex problems strongly depends on a proper identification of the aims, understanding the constraints and using domain knowledge as much as possible (Michalewicz, Fogel, How to solve it: mod

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Oblique and Mixed Decision Treesems are frequently overgrown and unstable [.]. Globally induced decision trees are smaller, as shown in the previous chapter, but still, for certain problems, especially when decision borders are not axis-parallel, room for improvement exists. However, this requires making a decision tree representa

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Cost-Sensitive Tree Inductioneated as equal whenever available. In real-world applications in medicine or business, such an idealization is not always possible. Often, a cost-sensitive prediction is desirable, and it should account for various cost types, starting with the asymmetric costs (losses) associated with predictive er

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