On the Handling of Continuous-Valued Attributes in Decision Tree Generation

Authors:
Usama M. Fayyad;Keki B. Irani
Affiliations:
Artificial Intelligence Laboratory, Electrical Engineering and Computer Science Department, The University of Michigan, Ann Arbor, MI 48109-2110. Present address: AI Group, M/S 525-3 ...;Artificial Intelligence Laboratory, Electrical Engineering and Computer Science Department, The University of Michigan, Ann Arbor, MI 48109-2110. IRANI@CAEN.ENGIN.UMICH.EDU
Venue:
Machine Learning
Year:
1992

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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.