A movel method for combining decision trees and kernel density estimators is proposed. Standard classification trees, or class probability trees, provide piecewise constant estimates of class posterior probabilities. Kernel density estimators can provide smooth non-parametric estimates of class probabilities, but scale poorly as the dimensionality of the problem increases. This paper discusses a hybrid scheme which uses decision trees to find the relevant structure in high-dimensional classification problems and then uses local kernel denisty estimates to fit smooth probability estimates within this structure. Experimental results on simulated data indicate that the method provides substantial improvement over trees or density methods alone for certain classes of problems. The paper briefly discusses various extensions of the basic approach and the types of application for which the method is best suited.
展开▼
