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Proceedings Paper

Fuzzy understanding of neighborhoods with nearest unlike neighbor sets
Author(s): Belur V. Dasarathy
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Paper Abstract

A new fuzzy learning and classification scheme based on developing a fuzzy understanding of neighborhoods with nearest unlike neighbor sets (NUNS) is reported in this study. NUNS, by definition, the set of samples identified as the nearest from the other class(es) for each given sample, represent in essence the boundaries between pattern classes known in the problem environment. Accordingly, samples close to the NUNS are likely to have more ambiguity or uncertainty in their labels than those farther away from these NUNS. This information about the uncertainty or imprecision in the labels of the given training set can be extracted and represented in terms of fuzzy memberships. These fuzzy membership values, which may be determined in the learning phase using appropriate fuzzy membership models, can then be utilized in the classification phase to derive the identity of an unknown sample. This classification can be accomplished using any one of the established fuzzy classification techniques.

Paper Details

Date Published: 13 June 1995
PDF: 10 pages
Proc. SPIE 2493, Applications of Fuzzy Logic Technology II, (13 June 1995); doi: 10.1117/12.211818
Show Author Affiliations
Belur V. Dasarathy, Dynetics, Inc. (United States)

Published in SPIE Proceedings Vol. 2493:
Applications of Fuzzy Logic Technology II
Bruno Bosacchi; James C. Bezdek, Editor(s)

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