Authors: Ashish Ghosh, B. Uma Shankar, Lorenzo Bruzzone, Saroj K. Meher
Addresses: Machine Intelligence Unit, Indian Statistical Institute, 203 BT Road, Kolkata 700108, India. ' Machine Intelligence Unit, Indian Statistical Institute, 203 BT Road, Kolkata 700108, India. ' Department of Information and Communication Technologies, University of Trento, Via Sommarive, 14, I-38050, Trento, Italy. ' Machine Intelligence Unit, Indian Statistical Institute, 203 BT Road, Kolkata 700108, India
Abstract: A neuro-fuzzy-combiner (NFC) is proposed to design an efficient multiple classifier system (MCS) with an aim to have an effective solution scheme for difficult classification problems. Although, a number of combiners exist in the literature, they do not provide consistently good performance on different datasets. In this scenario: 1) we propose an effective multiple classifier system (MCS) based on NFC that fuses the output of a set of fuzzy classifiers; 2) conduct an extensive experimental analysis to justify the effectiveness of the proposed NFC. In the proposed technique, we used a neural network to combine the output of a set of fuzzy classifiers using the principles of neuro-fuzzy hybridisation. The neural combiner adaptively learns its parameters depending on the input data, and thus the output is robust. Superiority of the proposed combiner has been demonstrated experimentally on five standard datasets and two remote sensing images. It performed consistently better than the existing combiners over all the considered datasets.
Keywords: multiple classifier systems; MCSs; classifier fusion; classifier combinations; neuro-fuzzy combiner; NFC; fuzzy classifiers; combination techniques; intermediate feature space; neural networks.
International Journal of Knowledge Engineering and Soft Data Paradigms, 2010 Vol.2 No.2, pp.107 - 129
Available online: 14 Aug 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article