Abstract #300924

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JSM 2003 Abstract #300924
Activity Number: 368
Type: Contributed
Date/Time: Wednesday, August 6, 2003 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract - #300924
Title: TARGET: Tree Analysis with Randomly Generated and Evolved Trees
Author(s): J. Brian Gray*+ and Guangzhe Fan
Companies: University of Alabama and University of Alabama
Address: Dept. of IS, Statistics, and Mgt Science, Tuscaloosa, AL, 35487-0001,
Keywords: genetic algorithm ; prediction ; classification ; regression ; bagging ; CART
Abstract:

Tree-structured modeling is a valuable tool for predictive modeling and data mining. However, traditional tree-growing methodologies, such as CART, are known to suffer from "greediness," i.e., local optimizations at nodes to be split in a decision tree do not always result in global optimization of the tree model. In addition, CART solutions are known to be sensitive to perturbations in the data and can vary greatly across different training sets sampled from the same data. Bagging and other ensemble techniques have improved on the predictive performance of CART, but they do not have the ease of interpretation of traditional tree models. We introduce a method of tree analysis based on randomly generated and evolved trees, known as TARGET. Empirical evidence suggests that (1) TARGET produces smaller trees than CART, (2) TARGET has better predictive performance than CART and the same or better predictive performance than bagging, (3) TARGET is more stable than CART across different training samples from the same data, and (4) the training and test misclassification rates of TARGET solutions are in closer agreement than those for CART, indicating less data overfitting.

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