Choquistic Regression: Generalizing Logistic Regression using the Choquet Integral
Ali Fallah Tehrani, Weiwei Cheng, Eyke Hüllermeier
Ali Fallah Tehrani
Available Online August 2011.
- https://doi.org/10.2991/eusflat.2011.86How to use a DOI?
- logistic regression, Choquet integral, monotone classification, attribute interaction
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 PB - Atlantis Press SP - 868 EP - 875 SN - 1951-6851 UR - https://doi.org/10.2991/eusflat.2011.86 DO - https://doi.org/10.2991/eusflat.2011.86 ID - Tehrani2011/08 ER -