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Fuzzy Integral Combination of One-Class Classifiers Designed for Multi-class Classification

  • Bilal HadjadjiEmail author
  • Youcef Chibani
  • Hassiba Nemmour
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8814)

Abstract

One-Class Classifier (OCC) has been widely used for its ability to learn without counterexamples. Its main advantage for multi-class is offering an open system and therefore allows easily extending new classes without retraining OCCs. Generally, pattern recognition systems designed by a single source of information suffer from limitations such as the lack of uniqueness and non-universality. Thus, combining information from multiple sources becomes a mode for designing pattern recognition systems. Usually, fixed rules such as average, product, minimum and maximum are the standard used combiners for OCC ensembles. However, fixed combiners cannot be useful to treat some difficult cases. Hence, we propose in this paper a combination scheme of OCCs based on the use of fuzzy integral (FI) operators. Experimental results conducted on different types of OCC and two different handwritten datasets prove the superiority of FI against fixed combiners for an open multi-class classification based on OCC ensemble.

Keywords

Combination Scheme Fuzzy Measure Speaker Verification Handwritten Digit MultiClass Classification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Bilal Hadjadji
    • 1
    Email author
  • Youcef Chibani
    • 1
  • Hassiba Nemmour
    • 1
  1. 1.Speech Communication and Signal Processing Laboratory, Faculty of Electronics and Computer ScienceUniversity of Science and Technology Houari Boumediene (USTHB)AlgiersAlgeria

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