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
Stepwise Induction of Model Trees
AI*IA 01 Proceedings of the 7th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Top-Down Induction of Model Trees with Regression and Splitting Nodes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Scalable look-ahead linear regression trees
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Simplification methods for model trees with regression and splitting nodes
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Learning model trees from evolving data streams
Data Mining and Knowledge Discovery
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Model trees are an extension of regression trees that associate leaves with multiple regression models. In this paper a method for the top-down induction 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 split nodes, which partition the sample space. The multiple linear model associated to each leaf is then obtained by combining straight-line regressions reported along the path from the root to the leaf. In this way, internal regression nodes contribute to the definition of multiple models and have a "global" effect, while straight-line regressions at leaves have only "local" effects. This peculiarity of SMOTI has been evaluated in an empirical study involving both real and artificial data.