• DocumentCode
    3059603
  • Title

    Neuro-Fuzzy Fusion: A New Approach to Multiple Classifier System

  • Author

    Meher, Saroj K. ; Ghosh, Ashish ; Shankar, B. Uma ; Bruzzone, Lorenzo

  • Author_Institution
    MIU, Kolkata
  • fYear
    2006
  • fDate
    18-21 Dec. 2006
  • Firstpage
    209
  • Lastpage
    212
  • Abstract
    Selection of a suitable classifier fusion scheme in the design of multiple classifier systems (MCSs) is a tedious task. To meet this we propose a neuro-fuzzy fusion (NFF) method for fusing the responses of a set of fuzzy classifiers. In the proposed method the output of the considered classifiers are fed to a neural network which performs the fusion task. Five labeled data sets, of which two are from remote sensing images, have been used for the performance comparison of various MCSs. Experimental study revealed the improved classification capability of the proposed NFF based MCS yielding consistently better results for all data sets.
  • Keywords
    fuzzy neural nets; fuzzy set theory; learning (artificial intelligence); pattern classification; classifier fusion; learning; multiple classifier system; neural network; neuro-fuzzy fusion; remote sensing images; Decision making; Design methodology; Fuzzy set theory; Fuzzy sets; Nearest neighbor searches; Neural networks; Pattern recognition; Remote sensing; Testing; Voting;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Technology, 2006. ICIT '06. 9th International Conference on
  • Conference_Location
    Bhubaneswar
  • Print_ISBN
    0-7695-2635-7
  • Type

    conf

  • DOI
    10.1109/ICIT.2006.67
  • Filename
    4273193