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Cross-oriented choquet integrals and their applications on data classification

Compared to the classification model based on single Choquet integrals, a novel generalized nonlinear classification model based on cross-oriented Choquet integrals is presented. A couple of Choquet integrals are used to achieve the classification boundaries which can classify data in situations such as one class surrounding another one in a high dimensional space. The classification problems come down to properly specifying the fuzzy measure with respect to which the Choquet integral(s) are defined and the classifying boundaries by which the different classes are separated. The values of these unknown parameters are optimally determined by an evolutionary computation. The performance of the presented model is compared and validated with some existed methods on a number of benchmark datasets.