Modeling Threshold Interaction Effects Through the Logistic Classification Trunk

Modeling Threshold Interaction Effects Through the Logistic Classification Trunk We introduce a model dealing with the identification of interaction effects in binary response data, which integrates recursive partitioning and generalized linear models. It derives from an ad-hoc specification and consequent implementation of the Simultaneous Threshold Interaction Modeling Algorithm (STIMA). The model, called Logistic Classification Trunk, allows us to obtain regression parameters by maximum likelihood through the simultaneous estimation of both main effects and threshold interaction effects. The main feature of this model is that it allows the user to evaluate a unique model and simultaneously the importance of both effects obtained by first growing a classification trunk and then by pruning it back to avoid overfitting. We investigate the choice of a suitable pruning parameter through a simulation study and compare the classification accuracy of the Logistic Classification Trunk with that of 13 alternative models/classifiers on 25 binary response datasets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Classification Springer Journals

Modeling Threshold Interaction Effects Through the Logistic Classification Trunk

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Publisher
Springer Journals
Copyright
Copyright © 2017 by Classification Society of North America
Subject
Statistics; Statistical Theory and Methods; Pattern Recognition; Bioinformatics; Signal,Image and Speech Processing; Psychometrics; Marketing
ISSN
0176-4268
eISSN
1432-1343
D.O.I.
10.1007/s00357-017-9241-y
Publisher site
See Article on Publisher Site

Abstract

We introduce a model dealing with the identification of interaction effects in binary response data, which integrates recursive partitioning and generalized linear models. It derives from an ad-hoc specification and consequent implementation of the Simultaneous Threshold Interaction Modeling Algorithm (STIMA). The model, called Logistic Classification Trunk, allows us to obtain regression parameters by maximum likelihood through the simultaneous estimation of both main effects and threshold interaction effects. The main feature of this model is that it allows the user to evaluate a unique model and simultaneously the importance of both effects obtained by first growing a classification trunk and then by pruning it back to avoid overfitting. We investigate the choice of a suitable pruning parameter through a simulation study and compare the classification accuracy of the Logistic Classification Trunk with that of 13 alternative models/classifiers on 25 binary response datasets.

Journal

Journal of ClassificationSpringer Journals

Published: Oct 6, 2017

References

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