Efficient Learning of Classifiers based on the 2-additive Choquet Integral

In a recent work, we proposed a generalization of logistic regression based on the Choquet integral. Our approach, referred to as choquistic regression, makes it 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. Unsurprisingly, these benefits come at the expense of an increased computational complexity of the underlying maximum likelihood estimation. In this paper, we propose two approaches for reducing this complexity in the specific though practically relevant case of the 2additive Choquet integral. Apart from theoretical results, we also present an experimental study in which we compare the two variants with the original implementation of choquistic regression.