Minimum splits based discretization for continuous features

Authors:
Ke Wang;Han Chong Goh
Affiliations:
Dept. of Information Systems and Computer Science, National University of Singapore, Singapore;Dept. of Information Systems and Computer Science, National University of Singapore, Singapore
Venue:
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Year:
1997

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Abstract

Discretization refers to splitting the range of continuous values into intervals so as to provide useful information about classes. This is usually done by minimizing a goodness measure, subject to constraints such as the maximal number of intervals, the minimal number of examples per interval, or some stopping criterion for splitting. We take a different approach by searching for minimum splits that minimize the number of intervals with respect to a threshold of impurity (i.e., badness). We propose a "total entropy" motivated selection of the "best" split from minimum splits, without requiring additional constraints. Experiments show that the proposed method produces better decision trees.