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EnglishModel 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.
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| Titolo: | Trading-off Local versus Global Effects of Regression Nodes in Model Trees | |
| Autori: | ||
| Data di pubblicazione: | 2002 | |
| Rivista: | ||
| Citazione: | 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. | |
| 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. | |
| Handle: | http://hdl.handle.net/11586/136701 | |
| ISBN: | 978-3-540-43785-7 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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