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.
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Yang, R., Wang, Z. (2012). Generalized Nonlinear Classification Model Based on Cross-Oriented Choquet Integral. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2012. Lecture Notes in Computer Science(), vol 7376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31537-4_3
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DOI: https://doi.org/10.1007/978-3-642-31537-4_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31536-7
Online ISBN: 978-3-642-31537-4
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