On the Handling of Continuous-Valued Attributes in Decision Tree Generation |
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Authors: | Usama M Fayyad Keki B Irani |
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Affiliation: | (1) Artificial Intelligence Laboratory, Electrical Engineering and Computer Science Department, The University of Michigan, Ann Arbor, MI, 48109-2110;(2) AI Group, M/S 525-3660, Jet Propulsion Lab, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA, 91109 |
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Abstract: | We present a result applicable to classification learning algorithms that generate decision trees or rules using the information entropy minimization heuristic for discretizing continuous-valued attributes. The result serves to give a better understanding of the entropy measure, to point out that the behavior of the information entropy heuristic possesses desirable properties that justify its usage in a formal sense, and to improve the efficiency of evaluating continuous-valued attributes for cut value selection. Along with the formal proof, we present empirical results that demonstrate the theoretically expected reduction in evaluation effort for training data sets from real-world domains. |
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Keywords: | Induction empirical concept learning decision trees information entropy minimization discretization classification |
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