Minimum splits based discretization for continuous features
Ke Wang, Han Chong Goh · 1997
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. 1 Introduction Continuous values refer to linearly ordered values, mainly numeric values. While continuous values are common in real applications, many learning algorithms focus on unordered discrete values. A common practice is to discretize continuous values into intervals so as t...