A greedy algorithm for supervised discretization
We present a greedy algorithm for supervised discretization using a metric defined on the space of partitions of a set of objects. This proposed technique is useful for preparing the data for classifiers that require nominal attributes. Experimental work on decision trees and nave Bayes classifiers confirm the efficacy of the proposed algorithm.
metric nave bayes classifiers generalized entropy boundary points