Greedy growing of tree-structured classification rules using a composite splitting criterion
Andrew B. Nobel · 2002
We establish the Bayes risk consistency of an unsupervised greedy-growing algorithm that produces tree-structured classifiers from labeled training vectors. The algorithm employs a composite splitting criterion equal to a weighted sum of Bayes risk and Euclidean distortion.