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Generalized Nonlinear Classification Model Based on Cross-Oriented Choquet Integral

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 7376)

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.

Keywords

  • classification
  • Choquet integral
  • signed efficiency measure
  • genetic algorithm
  • optimization

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© 2012 Springer-Verlag Berlin Heidelberg

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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

  • eBook Packages: Computer ScienceComputer Science (R0)