Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/99555
Title: Minimum splits based discretization for continuous features
Authors: Wang, K. 
Goh, H.C.
Issue Date: 1997
Source: Wang, K.,Goh, H.C. (1997). Minimum splits based discretization for continuous features. IJCAI International Joint Conference on Artificial Intelligence 2 : 942-947. ScholarBank@NUS Repository.
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.
Source Title: IJCAI International Joint Conference on Artificial Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/99555
ISSN: 10450823
Appears in Collections:Staff Publications

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