Effect of feature-type in selecting distance measure for an artificial immune system as a pattern recognizer

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dc.contributor.authorOezsen, Seral
dc.contributor.authorGuenes, Salih
dc.date.accessioned2020-03-26T17:26:42Z
dc.date.available2020-03-26T17:26:42Z
dc.date.issued2008
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIn designing an artificial immune system (AIS) for a problem domain, one must select a distance measure to find the affinity between system units and input data after determining a representation type. Euclidean distance is a commonly used distance measure in many proposed methods and is selected intuitively or due to simplicity of implementation. But this selection must be done carefully by considering the properties of problem domain. For example, most problems use data vectors with discrete, real-valued and nominal feature values. Whereas Euclidean distance can be used in this kind of problems, some other similarity measures designed for these hybrid vectors would give better results. To call attention of AIS designer to this point, we have tested three distance criteria which are Euclidean distance, Manhattan distance, and hybrid similarity measure on a simple AIS for the classification of two medical dataset taken from the UCI machine learning repository. One of the datasets, Statlog heart disease, contains nominal, discrete and real-valued vectors while the other one, BUPA liver disorders dataset, consists of purely real-valued vectors. For Statlog dataset, the best classification result was obtained with hybrid similarity measure as expected because this dataset consists of three-types of features while results for BUPA dataset were not different so much for the used measures, which is also an expected result considering the nature of this dataset. (C) 2007 Elsevier Inc. All rights reserved.en_US
dc.identifier.doi10.1016/j.dsp.2007.08.004en_US
dc.identifier.endpage645en_US
dc.identifier.issn1051-2004en_US
dc.identifier.issue4en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage635en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.dsp.2007.08.004
dc.identifier.urihttps://hdl.handle.net/20.500.12395/22336
dc.identifier.volume18en_US
dc.identifier.wosWOS:000256820200015en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherACADEMIC PRESS INC ELSEVIER SCIENCEen_US
dc.relation.ispartofDIGITAL SIGNAL PROCESSINGen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjecthybrid feature vectorsen_US
dc.subjectdistance measuresen_US
dc.subjectartificial immune systemsen_US
dc.subjectstatlog heart diseaseen_US
dc.subjectBUPA liver disordersen_US
dc.titleEffect of feature-type in selecting distance measure for an artificial immune system as a pattern recognizeren_US
dc.typeArticleen_US

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