Title :
CORT: classification or regression trees
Author :
Scott, Clayton D. ; Willett, Rebecca M. ; Nowak, Robert D.
Author_Institution :
Dept. of Electr. & Comput. Eng., Rice Univ., Houston, TX, USA
Abstract :
We challenge three of the underlying principles of CART, a well know approach to the construction of classification and regression trees (CART). Our primary concern is with the penalization strategy employed to prune back an initial, overgrown tree. We reason, based on both intuitive and theoretical arguments, that the pruning rule for classification should be different from that used for regression (unlike CART). We also argue that growing a tree-structured partition that is specifically fitted to the data is unnecessary. Instead, our approach to tree modeling begins with a nonadapted (fixed) dyadic tree structure and partition, much like that underlying multiscale wavelet analysis. We show that dyadic trees provide sufficient flexibility, are easy to construct, and produce near-optimal results when properly pruned. Finally, we advocate the use of a negative log-likelihood measure of empirical risk. This is a more appropriate empirical risk for non-Gaussian regression problems, in contrast to the sum-of-squared errors criterion used in CART regression.
Keywords :
regression analysis; signal classification; trees (mathematics); classification trees; dyadic tree partition; dyadic tree structure; empirical risk; multiscale wavelet analysis; negative log-likelihood measure; nonGaussian regression; overgrown tree pruning rule; regression trees; sum-of-squared errors criterion; tree-structured partition; Algorithm design and analysis; Classification tree analysis; Partitioning algorithms; Regression tree analysis; Training data; Tree data structures; Wavelet analysis;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on
Print_ISBN :
0-7803-7663-3
DOI :
10.1109/ICASSP.2003.1201641