Classification Trunk Approach (CTA) is a method for the automatic selection of threshold interactions in generalized linear modelling (GLM). It comes out from the integration of classification trees and GLM. Interactions between predictors are expressed as “threshold interactions” instead of traditional cross-products. Unlike classification trees, CTA is based on a different splitting criterion as well as on the possibility to “force” the first split of the tree selecting manually the first splitting predictor. CTA is framed in a new algorithm - STIMA - that can be used to estimate threshold interactions effects in classification and regression models. This paper specifically focuses on the binary response case, and presents the results of an application on the Livers Disorder dataset to give insight into the advantages deriving from the use of CTA with respect to other model-based or decision tree-based approaches. Performances of the different methods are compared focusing on prediction accuracy and model complexity.

Simultaneous threshold Interaction detection in binary classification

Conversano, C.;
2010

Abstract

Classification Trunk Approach (CTA) is a method for the automatic selection of threshold interactions in generalized linear modelling (GLM). It comes out from the integration of classification trees and GLM. Interactions between predictors are expressed as “threshold interactions” instead of traditional cross-products. Unlike classification trees, CTA is based on a different splitting criterion as well as on the possibility to “force” the first split of the tree selecting manually the first splitting predictor. CTA is framed in a new algorithm - STIMA - that can be used to estimate threshold interactions effects in classification and regression models. This paper specifically focuses on the binary response case, and presents the results of an application on the Livers Disorder dataset to give insight into the advantages deriving from the use of CTA with respect to other model-based or decision tree-based approaches. Performances of the different methods are compared focusing on prediction accuracy and model complexity.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11584/28258
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