Classifying high-dimensional patterns using a fuzzy logic discriminant network

Authors:
Nick J. Pizzi;Witold Pedrycz
Affiliations:
Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada;Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada
Venue:
Advances in Fuzzy Systems - Special issue on Hybrid Biomedical Intelligent Systems
Year:
2012

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Abstract

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.