| Title of paper:
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Bacillus colonies recognition using intuitionistic fuzzy sets
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| Author(s):
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| Vahid Khatibi
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| Information Technology Dept., School of Engineering, Tarbiat Modares University, P.O. Box 14115-179, Tehran, Iran
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| Gh. A. Montazer
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| Information Technology Dept., School of Engineering, Tarbiat Modares University, P.O. Box 14115-179, Tehran, Iran
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| montazer@modares.ac.ir (corresponding author)
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| Presented at:
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12th ICIFS, Sofia, 17—18 May 2008
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| Published in:
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"Notes on Intuitionistic Fuzzy Sets", Volume 14 (2008) Number 2, pages 91—99
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| Download:
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 PDF (247 Kb, File info)
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| Abstract:
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The pattern recognition problem in many practical domains such as medical diagnosis needs to encounter uncertain and imprecise hypotheses. Instead of solely relying on statistical inference for classification-type recognition, we need to quantify uncertainty first, and then, classify the unknown samples according to uncertainty quantification result. On the other hand, the Intuitionistic Fuzzy Sets provide a convenient framework for uncertainty quantification. Instead of measuring the similarity between certain pattern feature vectors and samples, the similarity between the Intuitionistic Fuzzy Sets of uncertain pattern feature vectors and samples are represented. In this paper, an IFS similarity measure is exploited in the practical problem of bacillus colonies classification. Our experiment showed that the classifier built on this approach could satisfactorily classify the unknown imprecise samples of bacillus colonies correctly, so as the final results were rational and acceptable.
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| Keywords:
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Intuitionistic fuzzy sets, IFS similarity measure, Pattern recognition, Medical diagnosis, Bacillus colony recognition
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