Author(s)

T. Urban, T. Kampke

Abstract

In this paper, we present a hierarchical approach to simultaneous regression and classification. Regression obviously becomes more accurate by assessing a regres- sion surface to each of a given class of a finite sample set compared to one regres- sion surface for the sample as a whole. For class formation, a tree of regression surfaces is constructed that balances minimization of the regression error and "sta- bilization" towards unseen data. Common tree-structured regression algorithms split nodes according to an inde- pendent variable. Terminal nodes correspond to one specific class. Such construc- tions gave rise to less complicated approaches that have mainly been used for adap- tive classification in machine learning. The considerable advantage of regression trees over a single regression is often set off by trees' poor behaviour on unseen data of su

Keywords