Model trees are tree-based regression models that associate leaves with linear regression models. A new method for the stepwise induction of model trees (SMOTI) has been developed. Its main characteristic is the construction of trees with two types of nodes: regression nodes, which perform only straight-line regression, and splitting nodes, which partition the feature space. In this way, internal regression nodes contribute to the definition of multiple linear models and have a "global" effect, while straight-line regressions at leaves have only "local" effects. In this paper the problem of simplifying model trees with both regression and splitting nodes is faced. In particular two methods, named Reduced Error Pruning (REP) and Reduced Error Grafting (REG), are proposed. They are characterized by the use of an independent pruning set. The effect of the simplification on model trees induced with SMOTI is empirically investigated. Results are in favour of simplified trees in most cases.

Simplification Methods for Model Trees with Regression and Splitting Nodes

CECI, MICHELANGELO;APPICE, ANNALISA;MALERBA, Donato
2003

Abstract

Model trees are tree-based regression models that associate leaves with linear regression models. A new method for the stepwise induction of model trees (SMOTI) has been developed. Its main characteristic is the construction of trees with two types of nodes: regression nodes, which perform only straight-line regression, and splitting nodes, which partition the feature space. In this way, internal regression nodes contribute to the definition of multiple linear models and have a "global" effect, while straight-line regressions at leaves have only "local" effects. In this paper the problem of simplifying model trees with both regression and splitting nodes is faced. In particular two methods, named Reduced Error Pruning (REP) and Reduced Error Grafting (REG), are proposed. They are characterized by the use of an independent pruning set. The effect of the simplification on model trees induced with SMOTI is empirically investigated. Results are in favour of simplified trees in most cases.
3-540-40504-6
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/136665
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 0
social impact