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In this paper, we elaborate on the use of the Sugeno integral in the contextof machine learning. More specifically, we propose a method for binaryclassification, in which the Sugeno integral is used as an aggregation functionthat combines several local evaluations of an instance, pertaining to differentfeatures or measurements, into a single global evaluation. Due to the specificnature of the Sugeno integral, this approach is especially suitable forlearning from ordinal data, that is, when measurements are taken from ordinalscales. This is a topic that has not received much attention in machinelearning so far. The core of the learning problem itself consists ofidentifying the capacity underlying the Sugeno integral. To tackle thisproblem, we develop an algorithm based on linear programming. The algorithmalso includes a suitable technique for transforming the original feature valuesinto local evaluations (local utility scores), as well as a method for tuning athreshold on the global evaluation. To control the flexibility of theclassifier and mitigate the problem of overfitting the training data, wegeneralize our approach toward $k$-maxitive capacities, where $k$ plays therole of a hyper-parameter of the learner. We present experimental studies, inwhich we compare our method with competing approaches on several benchmark datasets.

Machine Learning with the Sugeno Integral: The Case of Binary Classification