Kernel-Induced Classification Trees and Random Forests
Guangzhe Fan
Department of Statistics and Actuarial Science
University of Waterloo

Abstract
A recursive-partitioning procedure using kernel functions is proposed
for classification problems. We call it KICT- kernel-induced
classification trees. Essentially, KICT uses kernel functions to
construct CART models. The resulting model could perform significantly
better in classification than the original CART model in many
situations, especially when the pattern of the data is non-linear. We
also introduce KIRF: kernel-induced random forests. KIRF compares
favorably to random forests and SVM in many situations. KICT and KIRF
also largely retain the computational advantage of CART and random
forests, respectively, in contrast to SVM. We use simulated and real
world data to illustrate their performances. We conclude that the
proposed methods are useful alternatives and competitors to CART,
random forests, and SVM.