Boosting methodology for regression problems.

Abstract

Classification problems have dominated research on boosting to date. The application of boosting to regression problems, on the other hand, has received little investigation. In this paper we develop a new boosting method for regression problems. We cast the regression problem as a classification problem and apply an interpretable form of the boosted naïve Bayes classifier. This induces a regression model that we show to be expressible as an additive model for which we derive estimators and discuss computational issues. We compare the performance of our boosted naïve Bayes regression model with other interpretable multivariate regression procedures. 1. INTRODUCTION In a wide variety of classification problems, boosting techniques have proven to be an effective method for reducing bias and variance, and improving misclassification rates (Bauer and Kohavi [1998]). While more evidence compiles about the utility of these techniques in classification problems little is known about their e...