An Asymptotically Optimal Tree Classifier Training Algorithm

Conventional non-parametric tree classifier design is characterized by partition-edit,where each subregion is assigned to a class according to voting by the patterns in thisregion.This scheme usually does not result in high recognition rate.An edit-partitionscheme is given in this paper,which achieves asymptotically Bayesian optimal recogni-tion rate.Simulation experiments have been conducted,which show that tree classif-iers in this scheme achieve high recognition rate even when the pattern distribution iscomplex and the training sample size is small.Furthermore the time efficiency of edit-partition tree classifiers is 100 to 1000 times higher than that of the other non-param-etric classifier,e.g.the nearest neighbor rules.