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Improving classifier accuracy by simulating fuzzy boundaries between classes

  • SUNY Buffalo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The pattern classification problem can be defined as one of assigning a label to a pattern of unknown class based on labelled prototype patterns. The method described in this paper is based on the following two ideas which appeal to our common sense: when the correctness of a classifier on a pattern x is in question, it is best to consider the performance of the same classifier on the patterns which are similar to x; and a classifier is usually accurate when the test pattern x falls close to the center of its class in feature space and prone to error when it falls near a class boundary.

Original languageEnglish
Title of host publication1998 Conference of the North American Fuzzy Information Processing Society, NAFIPS 1998
EditorsLawrence O. Hall, Jim Bezdek
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages161-164
Number of pages4
ISBN (Electronic)0780344537
DOIs
StatePublished - 1998
Event1998 Conference of the North American Fuzzy Information Processing Society, NAFIPS 1998 - Pensacola Beach, United States
Duration: Aug 20 1998Aug 21 1998

Publication series

NameAnnual Conference of the North American Fuzzy Information Processing Society - NAFIPS

Conference

Conference1998 Conference of the North American Fuzzy Information Processing Society, NAFIPS 1998
Country/TerritoryUnited States
CityPensacola Beach
Period08/20/9808/21/98

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