1998
DOI: 10.1007/bfb0094810
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Cost sensitive discretization of numeric attributes

Abstract: Abstract. Many algorithms in decision tree learning are not designed to handle numeric valued attributes very well. Therefore, discretization of the continuous feature space has to be carried out. In this article we introduce the concept of cost sensitive discretization as a preprocessing step to induction of a classifier and as an elaboration of the error-based discretization method to obtain an optimal multi-interval splitting for each numeric attribute. A transparant description of the method and steps invo… Show more

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Cited by 6 publications
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