Classifying High-Dimensional Patterns Using a Fuzzy Logic Discriminant Network
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Date
2012-3-4
Authors
Pizzi, Nick J.
Pedrycz, Witold
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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.
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Nick J. Pizzi and Witold Pedrycz, “Classifying High-Dimensional Patterns Using a Fuzzy Logic Discriminant Network,” Advances in Fuzzy Systems, vol. 2012, Article ID 920920, 7 pages, 2012. doi:10.1155/2012/920920