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.

Trading-off Local versus Global Effects of Regression Nodes in Model Trees / MALERBA D.; APPICE A.; CECI M; MONOPOLI M.. - 2366(2002), pp. 393-402. ((Intervento presentato al convegno 13th International Symposium on Methodologies for Intelligent Sistems (ISMIS'02) tenutosi a Lyon, France nel June 27-29.

Trading-off Local versus Global Effects of Regression Nodes in Model Trees

MALERBA, Donato;APPICE, ANNALISA;CECI, MICHELANGELO;
2002

Abstract

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.
978-3-540-43785-7
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/136701
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