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Classifying High-Dimensional Patterns Using a Fuzzy Logic Discriminant Network
Joint Authors
Source
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-03-04
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Pizzi, Nick J.& Pedrycz, Witold. 2012. Classifying High-Dimensional Patterns Using a Fuzzy Logic Discriminant Network. Advances in Fuzzy Systems،Vol. 2012, no. 2012, pp.1-7.
https://search.emarefa.net/detail/BIM-508275
Modern Language Association (MLA)
Pizzi, Nick J.& Pedrycz, Witold. Classifying High-Dimensional Patterns Using a Fuzzy Logic Discriminant Network. Advances in Fuzzy Systems No. 2012 (2012), pp.1-7.
https://search.emarefa.net/detail/BIM-508275
American Medical Association (AMA)
Pizzi, Nick J.& Pedrycz, Witold. Classifying High-Dimensional Patterns Using a Fuzzy Logic Discriminant Network. Advances in Fuzzy Systems. 2012. Vol. 2012, no. 2012, pp.1-7.
https://search.emarefa.net/detail/BIM-508275
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-508275