Fuzzy understanding of neighborhoods with nearest unlike neighbor sets

Belur V. Dasarathy

doi:10.1117/12.211818

13 June 1995 Fuzzy understanding of neighborhoods with nearest unlike neighbor sets

Belur V. Dasarathy

Proceedings Volume 2493, Applications of Fuzzy Logic Technology II; (1995) https://doi.org/10.1117/12.211818
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

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.

Citation Download Citation

Belur V. Dasarathy "Fuzzy understanding of neighborhoods with nearest unlike neighbor sets", Proc. SPIE 2493, Applications of Fuzzy Logic Technology II, (13 June 1995); https://doi.org/10.1117/12.211818

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