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 and it 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 Liver Disorders 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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References
Breiman, L. (1996). Bagging predictors. Machine Learning, 24, 123–140.
Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Belmont, CA: Wadsworth.
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd edition). Mahwah NJ: Lawrence Erlbaum.
de Gonzalez, A. B., & Cox, D. R. (2007). Interpretation of interaction: A review. Annals of Applied Statistics, 1(2), 371–375.
Dusseldorp, E., & Meulman, J. (2004). The regression trunk approach to discover treatment covariate interactions. Psychometrika, 69, 355–374.
Dusseldorp, E., Spinhoven, P., Bakker, A., Van Dyck, R., & Van Balkom, A. J. L. M. (2007). Which panic disorder patients benefit from which treatment: Cognitive therapy or antidepressants? Psychotherapy and Psychosomatics, 76, 154–161.
Dusseldorp, E., Conversano, C., & Van Os, B. J. (2009). Combining an Additive and tree-based regression model simulatenously: STIMA, Journal of Computational and Graphical Statistics, to appear.
Fahrmeir, L., & Tutz, G. (2001). Multivariate statistical modelling based on generalized linear models (2nd edition). New York: Springer.
Freund, Y., & Schapire, R. (1997). A decision-theoretic generalization of on-line learning and an application to Boosting. Journal of Computer and System Sciences, 55(1), 119–139.
Friedman, J. H. (1991). Multivariate adaptive regression splines (with discussion). Annals of Statistics, 19, 1–141.
Hastie, T. J., & Tibshirani, R. J. (1990). Generalized additive models. London: Chapman & Hall.
McCullagh, P., & Nelder, J. A. (1989). Generalized linear models (2nd edition). London: Chapman & Hall.
Vapnik, V. (1998). Statistical learning theory. New York: Wiley.
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Conversano, C., Dusseldorp, E. (2010). Simultaneous Threshold Interaction Detection in Binary Classification. In: Palumbo, F., Lauro, C., Greenacre, M. (eds) Data Analysis and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03739-9_26
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DOI: https://doi.org/10.1007/978-3-642-03739-9_26
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