Generalized Nonlinear Classification Model Based on Cross-Oriented Choquet Integral

A generalized nonlinear classification model based on cross-oriented Choquet integrals is presented. A couple of Choquet integrals are used in this model to achieve the classification boundaries which can classify data in such situation as one class surrounding another one in a high dimensional space. The values of unknown parameters in the generalized model are optimally determined by a genetic algorithm based on a given training data set. Both artificial experiments and real case studies show that this generalized nonlinear classifier based on cross-oriented Choquet integrals improves and extends the functionality of traditional classifier based on one Choquet integral on solving the classification problems of multi-class multi-dimensional situations.