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|Title:||Minimum splits based discretization for continuous features|
|Authors:||Wang, K. |
|Citation:||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|
|Appears in Collections:||Staff Publications|
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