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  • Research Article
  • Open Access

Incremental Local Linear Fuzzy Classifier in Fisher Space

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  • 1, 2Email author,
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EURASIP Journal on Advances in Signal Processing20092009:360834

Received: 4 August 2008

Accepted: 25 March 2009

Published: 10 May 2009


Optimizing the antecedent part of neurofuzzy system is an active research topic, for which different approaches have been developed. However, current approaches typically suffer from high computational complexity or lack of ability to extract knowledge from a given set of training data. In this paper, we introduce a novel incremental training algorithm for the class of neurofuzzy systems that are structured based on local linear classifiers. Linear discriminant analysis is utilized to transform the data into a space in which linear discriminancy of training samples is maximized. The neurofuzzy classifier is then built in the transformed space, starting from the simplest form (a global linear classifier). If the overall performance of the classifier was not satisfactory, it would be iteratively refined by incorporating additional local classifiers. In addition, rule consequent parameters are optimized using a local least square approach. Our refinement strategy is motivated by LOLIMOT, which is a greedy partition algorithm for structure training and has been successfully applied in a number of identification problems. The proposed classifier is compared to several benchmark classifiers on a number of well-known datasets. The results prove the efficacy of the proposed classifier in achieving high performance while incurring low computational effort.


  • Linear Discriminant Analysis
  • High Computational Complexity
  • Linear Classifier
  • Refinement Strategy
  • Local Classifier

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Authors’ Affiliations

Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran
Unité de Génie Biophysique et Médical, Groupe de Recherche sur l'Analyse Multimodale de la Fonction Cérébrale (GRAMFC), Faculté de Médecine, AMIENS cedex, France
School of Information Technology and Engineering, University of Ottawa, Ottawa, Canada
Department of Electrical and Computer Engineering, Ryerson University, Toronto, Canada


© Armin Eftekhari et al. 2009

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.