Discretization of Continuous Attributes for Learning Classification Rules
We present a comparison of three entropy-based discretization methods in a context of learning classification rules. We compare the binary recursive discretization with a stopping criterion based on the Minimum Description Length Principle (MDLP), a non-recursive method which simply chooses a number of cut-points with the highest entropy gains, and a non-recursive method that selects cut-points according to both information entropy and distribution of potential cut-points over the instance space. Our empirical results show that the third method gives the best predictive performance among the three methods tested.
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