Classifying High-Dimensional Patterns Using a Fuzzy Logic Discriminant Network

Advances in Fuzzy Systems. 2012;2012 DOI 10.1155/2012/920920

 

Journal Homepage

Journal Title: Advances in Fuzzy Systems

ISSN: 1687-7101 (Print); 1687-711X (Online)

Publisher: Hindawi Limited

LCC Subject Category: Technology: Electrical engineering. Electronics. Nuclear engineering | Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML, ePUB, XML

 

AUTHORS

Nick J. Pizzi (Department of Computer Science, University of Manitoba, Winnipeg MB, R3T 2N2, Canada)
Witold Pedrycz (Department of Electrical and Computer Engineering, University of Alberta, Edmonton AB, T6R 2G7, Canada)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 21 weeks

 

Abstract | Full Text

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.