Ordinal Choquistic Regression (original) (raw)
Proceedings of the 8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-13)
Authors
Ali Fallah Tehrani, Eyke Huellermeier
Available Online August 2013.
DOI
10.2991/eusflat.2013.119How to use a DOI?
Keywords
logistic regression ordinal classification Choquet integral monotone classification attribute interaction
Abstract
We propose an extension of choquistic regression from the case of binary to the case of ordinal classification. Choquistic regression itself has been introduced recently as a generalization of conventional logistic regression. The basic idea of this method is to replace the linear function of predictor variables in the logistic regression model by the Choquet integral. Thus, it becomes possible to capture nonlinear dependencies and interactions among predictor variables while preserving two important properties of logistic regression, namely the comprehensibility of the model and the possibility to ensure its monotonicity in individual predictors. In experimental studies, choquistic regression consistently improves upon standard logistic regression in terms of predictive accuracy, especially when being combined with a novel regularization technique that prevents from exceeding the required level of nonadditivity.
Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Ali Fallah Tehrani AU - Eyke Huellermeier PY - 2013/08 DA - 2013/08 TI - Ordinal Choquistic Regression BT - Proceedings of the 8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-13) PB - Atlantis Press SP - 842 EP - 849 SN - 1951-6851 UR - https://doi.org/10.2991/eusflat.2013.119 DO - 10.2991/eusflat.2013.119 ID - Tehrani2013/08 ER -