Classifying High-Dimensional Patterns Using a Fuzzy Logic Discriminant Network

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dc.contributor.authorPizzi, Nick J.
dc.contributor.authorPedrycz, Witold
dc.date.accessioned2015-05-14T16:39:12Z
dc.date.available2015-05-14T16:39:12Z
dc.date.issued2012-3-4
dc.date.updated2015-03-29T13:33:52Z
dc.description.abstractAlthough 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.
dc.description.versionPeer Reviewed
dc.identifier.citationNick 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
dc.identifier.urihttp://dx.doi.org/10.1155/2012/920920
dc.identifier.urihttp://hdl.handle.net/1993/30506
dc.language.rfc3066en
dc.rightsopen accessen_US
dc.rights.holderCopyright © 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.
dc.titleClassifying High-Dimensional Patterns Using a Fuzzy Logic Discriminant Network
dc.typeJournal Article
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