Kittakorn Sriwanna

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Discretization plays a major role as a data preprocessing technique used in machine learning and data mining. Recent studies have focused on multivariate discretization that considers relations among attributes. The general goal of this method is to obtain the discrete data, which preserves most of the semantics exhibited by original continuous data.(More)
Most discretization algorithms focus on the univariate case, which proceeds without considering interactions among attributes. Furthermore, they find the value of discretization criterion based on a greedy method that usually leads to a sub-optimal set of cut-points. In response, this paper introduces an evolutionary cut points search for graph(More)
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