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Oblique Multicategory Decision Trees Using Nonlinear Programming

Induction of decision trees is a popular and effective method for solving classification problems in data-mining applications. This paper presents a new algorithm for multi-category decision tree induction based on nonlinear programming. This algorithm, termed OC-SEP (Oblique Category SEParation), combines the advantages of several other methods and shows improved generalization performance on a collection of real-world data sets.