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dc.contributor.authorBunke, Horst
dc.contributor.authorFàbregas, Xavier
dc.contributor.authorKandel, Abraham
dc.description.abstractA new similarity measure for objects that are represented by feature vectors of fixed dimension is introduced. It can simultaneously deal with numeric and symbolic features. Also, it can tolerate missing feature values. The similarity measure between two objects is described in terms of the similarity of their features. IF-THEN rules are being used to model the individual contribution of each feature to the global similarity measure between a pair of objects. The proposed similarity measure is based on fuzzy sets and this allows us to deal with vague, uncertain and distorted information in a natural way. Several formal properties of the proposed similarity measure are derived; in particular, we show that the measure can be used to model the Euclidean distance as well as other, non-Euclidean distance functions. Also, an application of the proposed similarity measure to nearest-neighbor classification in a medical expert system is described.
dc.publisherUniversitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica
dc.relation.ispartofMathware & soft computing . 2001 Vol. 8 Núm. 2
dc.rightsReconeixement-NoComercial-CompartirIgual 3.0 Espanya
dc.subject.otherEuclidean distance
dc.subject.otherNearest-neighbor classifier
dc.subject.otherFuzzy linguistic variable
dc.subject.otherFuzzy inference,
dc.subject.otherCase-based reasoning
dc.subject.otherMedical expert systems
dc.subject.otherThyroid gland diagnosis
dc.titleRule-based fuzzy object similarity
dc.subject.lemacIntel·ligència artificial
dc.subject.lemacMedicina -- Decisió, Presa de -- Processament de dades
dc.subject.lemacTiroide -- Malalties -- Diagnòstic
dc.subject.amsClassificació AMS::68 Computer science::68T Artificial intelligence
dc.rights.accessOpen Access

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