Choquistic Regression: Generalizing Logistic Regression using the Choquet Integral

DOI

10.2991/eusflat.2011.86How to use a DOI?

Keywords

logistic regression, Choquet integral, monotone classification, attribute interaction

Abstract

In this paper, we propose a generalization of logistic regression based on the Choquet integral. The basic idea of our approach, referred to as choquistic regression, is to replace the linear function of predictor variables, which is commonly used in logistic regression to model the log odds of the positive class, by the Choquet integral. Thus, it becomes possible to capture non-linear 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 with real and benchmark data, choquistic regression consistently improves upon standard logistic regression in terms of predictive accuracy.

Copyright

© 2011, 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/).

Download article (PDF)

TY  - CONF
AU  - Ali Fallah Tehrani
AU  - Weiwei Cheng
AU  - Eyke Hüllermeier
PY  - 2011/08
DA  - 2011/08
TI  - Choquistic Regression: Generalizing Logistic Regression using the Choquet Integral
BT  - Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-11)
PB  - Atlantis Press
SP  - 868
EP  - 875
SN  - 1951-6851
UR  - https://doi.org/10.2991/eusflat.2011.86
DO  - 10.2991/eusflat.2011.86
ID  - Tehrani2011/08
ER  -