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Construction of Fuzzy Signature from Data: An Example of SARS Pre-clinical Diagnosis System

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dc.contributor.authorWong, Kok Wai
dc.contributor.authorGedeon, Tamas (Tom)
dc.contributor.authorKoczy, Laszlo T.
dc.coverage.spatialBudapest Hungary
dc.date.accessioned2015-12-08T22:29:21Z
dc.date.createdJuly 25-29 2004
dc.date.issued2004
dc.date.updated2015-12-08T09:21:35Z
dc.description.abstractThere are many areas where objects with very complex and sometimes interdependent features are to be classified; similarities and dissimilarities are to be evaluated. This makes a complex decision model difficult to construct effectively. Fuzzy signatures are introduced to handle complex structured data and interdependent feature problems. Fuzzy signatures can also used in cases where data is missing. This paper presents the concept of a fuzzy signature and how its flexibility can be used to quickly construct a medical pre-clinical diagnosis system. A Severe Acute Respiratory Syndrome (SARS) pre-clinical diagnosis system using fuzzy signatures is constructed as an example to show many advantages of the fuzzy signature. With the use of this fuzzy signature structure, complex decision models in the medical field should be able to be constructed more effectively.
dc.identifier.isbn1780383532
dc.identifier.urihttp://hdl.handle.net/1885/34071
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE Inc)
dc.relation.ispartofseriesIEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2004)
dc.sourceProceedings of the 2004 IEEE International Conference on Fuzzy Systems
dc.source.urihttp://ieeexplore.ieee.org.virtual.anu.edu.au/xpl/tocresult.jsp?isnumber=30018&isYear=2004&count=95&page=1&ResultStart=25
dc.subjectKeywords: Complex decision model; Data mining algorithms; Fuzzy signatures; Severe acute respiratory syndrome (SARS); Algorithms; Data mining; Decision theory; Fuzzy sets; Mathematical models; Problem solving; Vectors; Database systems
dc.titleConstruction of Fuzzy Signature from Data: An Example of SARS Pre-clinical Diagnosis System
dc.typeConference paper
local.bibliographicCitation.lastpage1654
local.bibliographicCitation.startpage1649
local.contributor.affiliationWong, Kok Wai, Nanyang Technological University
local.contributor.affiliationGedeon, Tamas (Tom), College of Engineering and Computer Science, ANU
local.contributor.affiliationKoczy, Laszlo T., Budapest University of Technology and Economics
local.contributor.authoruidGedeon, Tamas (Tom), u4088783
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080108 - Neural, Evolutionary and Fuzzy Computation
local.identifier.ariespublicationU3594520xPUB109
local.identifier.doi10.1109/FUZZY.2004.1375428
local.identifier.scopusID2-s2.0-11144321607
local.type.statusPublished Version

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