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A Recursive Partitioning Decision Rule for Nonparametric Classification

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1 Author(s)
Friedman, J.H. ; Stanford Linear Accelerator Center

A new criterion for deriving a recursive partitioning decision rule for nonparametric classification is presented. The criterion is both conceptually and computationally simple, and can be shown to have strong statistical merit. The resulting decision rule is asymptotically Bayes' risk efficient. The notion of adaptively generated features is introduced and methods are presented for dealing with missing features in both training and test vectors.

Published in:

Computers, IEEE Transactions on  (Volume:C-26 ,  Issue: 4 )