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Advances in Fuzzy Systems
Volume 2012, Article ID 920920, 7 pages
Research Article

Classifying High-Dimensional Patterns Using a Fuzzy Logic Discriminant Network

1Department of Computer Science, University of Manitoba, Winnipeg MB, Canada R3T 2N2
2Department of Electrical and Computer Engineering, University of Alberta, Edmonton AB, Canada T6R 2G7

Received 28 July 2011; Accepted 8 December 2011

Academic Editor: Maysam Abbod

Copyright © 2012 Nick J. Pizzi and Witold Pedrycz. 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.


Although many classification techniques exist to analyze patterns possessing straightforward characteristics, they tend to fail when the ratio of features to patterns is very large. This “curse of dimensionality” is especially prevalent in many complex, voluminous biomedical datasets acquired using the latest spectroscopic modalities. To address this pattern classification issue, we present a technique using an adaptive network of fuzzy logic connectives to combine class boundaries generated by sets of discriminant functions. We empirically evaluate the effectiveness of this classification technique by comparing it against two conventional benchmark approaches, both of which use feature averaging as a preprocessing phase.