Using Generalized Estimating Equation to Learn Decision Tree with Multivariate Responses

摘要

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.