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 dependen...

Bi-capacities arise as a natural generalization of capacities (or fuzzy measures) in a context of decision making where underlying scales are bipolar. They are able to capture a wide variety of decision behaviours, encompassing models such as Cumulative Prospect Theory (CPT). The aim of this paper in two parts is to present the machinery behind bicapacities, and thus remains on a rather theoret...

The paper introduces a new class of functions from [0,1]n to [0,n] called d-Choquet integrals. These are generalization the “standard” Choquet integral obtained by replacing difference in definition usual dissimilarity function. In particular, all integrals encompasses but use dissimilarities provides higher flexibility and generality. We show that some aggregation/pre-aggregation/averaging/fun...