Computational Statistics & Data Analysis - Data analysis and inference in nonstandard settings
Functional Models for Regression Tree Leaves
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Trading-Off Local versus Global Effects of Regression Nodes in Model Trees
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
A New Cluster Based Fuzzy Model Tree for Data Modeling
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
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Regression trees are tree-based models used to solve those prediction problems in which the response variable is numeric. They differ from the better-known classification or decision trees only in that they have a numeric value rather than a class label associated with the leaves. Model trees are an extension of regression trees in the sense that they associate leaves with multivariate linear models. In this paper a method for the data-driven construction of model trees is presented, namely the Stepwise Model Tree Induction (SMOTI) method. Its main characteristic is the induction of trees with two types of nodes: regression nodes, which perform only straight-line regression, and splitting nodes, which partition the sample space. In this way, the multivariate linear model associated to each leaf is efficiently built stepwise. SMOTI has been evaluated in an empirical study and compared to other model tree induction systems.