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
Link To Document