Issue:Bacillus colonies recognition using intuitionistic fuzzy sets

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Title of paper: Bacillus colonies recognition using intuitionistic fuzzy sets Author(s): Vahid Khatibi Information Technology Dept., School of Engineering, Tarbiat Modares University, P.O. Box 14115-179, Tehran, Iran Gh. A. Montazer Information Technology Dept., School of Engineering, Tarbiat Modares University, P.O. Box 14115-179, Tehran, Iran montazer@modares.ac.ir (corresponding author) Presented at: 12th ICIFS, Sofia, 17—18 May 2008 Published in: "Notes on Intuitionistic Fuzzy Sets", Volume 14 (2008) Number 2, pages 91—99 Download:  PDF (247  Kb, File info) Abstract: 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. Keywords: Intuitionistic fuzzy sets, IFS similarity measure, Pattern recognition, Medical diagnosis, Bacillus colony recognition References: K. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems 20(1) (1986) 87-96. K. Atanassov, More on intuitionistic fuzzy sets, Fuzzy Sets and Systems 33(1) (1989) 37-46. K. Atanassov, Intuitionistic Fuzzy Sets, Physica-Verlag, Heidelberg, 1999. H. Bustince, P. Burillo, Vague sets are intuitionistic fuzzy sets, Fuzzy Sets and Systems 79(3) (1996) 403-405. S.K. De, R. Biswas, R. Roy, An application of intuitionistic fuzzy sets in medical diagnosis, Fuzzy Sets and Systems 117(2) (2001) 209-213. L. Deng-Feng, C. Chuntian, New similarity measures of intuitionistic fuzzy sets and applications to pattern recognitions, Pattern Recognition Letters 23 (2002) 221-225. P.A. Devijer, J. Kittler, Pattern Recognition, A Statistical Approach, Prentice-Hall, London, England, 1982. R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification, 2nd ed, Wiley-Inetscience, New York, 2000. H. Facklam, M. Facklam, Bacteria, Twenty-First Century, 1995. D. Li, C. Cheng, New similarity measures of intuitionistic fuzzy sets and application to pattern recognition, Pattern Recognition Letters 23(1-3) (2002) 221-225. Z. Liang, P. Shi, Similarity measures for intuitionistic fuzzy sets, Pattern Recognition Letters 24 (2003) 2687–2693. H.B. Mitchell, Pattern recognition using type-II fuzzy sets, Information Sciences 170 (2005) 409–418. S.K. Pal, P. Mitra, Pattern Recognition Algorithms and Data Minig, Chapman and Hall/CRC, Florida, 2004. E.R. Ricciuti, Microorganisms: The Unseen World, Blackbirch, 1993. N. Sankaran, Microbes and People: An A-Z of Microorganisms in Our Lives, Oryx, 2001. E. Szmidt, J. Kacprzyk, Distances between intuitionistic fuzzy sets, Fuzzy Sets and Systems 114(3) (2000) 505-518. W. Wang, X. Xin, Distance measure between intuitionistic fuzzy sets, Pattern Recognition Letters 26 (2005) 2063-2069. L.A. Zadeh, Fuzzy sets, Inform. Control 8 (1965) 338-353. L.A. Zadeh, Fuzzy Logic, neural networks, and soft computing, Comm. ACM 37 (1994) 77-84 Citations: The list of publications, citing this article may be empty or incomplete. If you can provide relevant data, please, write on the talk page.