Generalized nonlinear classification model based on cross-oriented choquet integral

Authors:
Rong Yang;Zhenyuan Wang
Affiliations:
College of Mechatronics and Control Engineering, Shen Zhen University, Shen Zhen, China;Department of Mathematics, University of Nebraska at Omaha
Venue:
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2012

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Abstract

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.