Using Generalized Estimating Equation to Learn Decision Tree with Multivariate Responses

Authors:
Seong Keon Lee;Hyun-Cheol Kang;Sang-Tae Han;Kwang-Hwan Kim
Affiliations:
Institute of Statistics, Korea University, Seoul, Korea 136-701;Department of Informational Statistics, Hoseo University, Asan, Korea 336-795;Department of Informational Statistics, Hoseo University, Asan, Korea 336-795;Department of Medical Record, Dankuk University Hospital, Chonan, Korea 330-715
Venue:
Data Mining and Knowledge Discovery
Year:
2005

Citing 4
Cited 0

Interactive machine learning: letting users build classifiers

International Journal of Human-Computer Studies
SAS Macro Language, Reference

SAS Macro Language, Reference
Induction of Decision Trees

Machine Learning
Categorical data analysis using the sas® system, 2nd edition

Categorical data analysis using the sas® system, 2nd edition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Previous decision tree algorithms have used Mahalanobis distance for multiple continuous longitudinal response or generalized entropy index for multiple binary responses. However, these methods are limited to either continuous or binary responses. In this paper, we suggest a new tree-based method that can analyze any type of multiple responses by using a statistical approach, called GEE (generalized estimating equations). The value of this new technique is demonstrated with reference to an application using web-usage survey.