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• W1861764418 abstract "Before applying learning algorithms to datasets, practitioners often globally discretize any numeric attributes. If the algorithm cannot handle numeric attributes directly, prior discretization is essential. Even if it can, prior discretization often accelerates induction, and may produce simpler and more accurate classi ers. As it is generally done, global discretization denies the learning algorithm any chance of taking advantage of the ordering information implicit in numeric attributes. However, a simple transformation of discretized data preserves this information in a form that learners can use. We show that, compared to using the discretized data directly, this transformation signi cantly increases the accuracy of decision trees built by C4.5, decision lists built by PART, and decision tables built using the wrapper method, on several benchmark datasets. Moreover, it can signi cantly reduce the size of the resulting classi ers. This simple technique makes global discretization an even more useful tool for data preprocessing." @default.
• W1861764418 created "2016-06-24" @default.
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• W1861764418 date "1999-06-27" @default.
• W1861764418 modified "2023-09-23" @default.
• W1861764418 title "Making Better Use of Global Discretization" @default.
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