A granular, parametric KNN classifier

dc.contributor.author	Τσούκαλας, Βασίλειος Θ.	el
dc.contributor.author	Καµπουρλάζος, Βασίλειος Γ.	el
dc.contributor.author	Σκουρλάς, Χρήστος Π.	el
dc.date.accessioned	2015-05-24T18:21:03Z
dc.date.available	2015-05-24T18:21:03Z
dc.date.issued	2015-05-24
dc.identifier.uri	http://hdl.handle.net/11400/11062
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://dl.acm.org/citation.cfm?doid=2491845.2491892	el
dc.subject	Classification
dc.subject	Granular computing
dc.subject	Ταξινόμηση
dc.subject	Κοκκώδης πληροφορική
dc.subject	Interval's number (IN)
dc.subject	Αριθμός διαστημάτων
dc.subject	K nearest neighbor (KNN)
dc.subject	Lattice computing (LC)
dc.subject	Lattice πληροφορική
dc.title	A granular, parametric KNN classifier	en
heal.type	conferenceItem
heal.classification	Computer science
heal.classification	Mathematics
heal.classification	Πληροφορική
heal.classification	Μαθηματικά
heal.classificationURI	http://skos.um.es/unescothes/C00750
heal.classificationURI	http://skos.um.es/unescothes/C02437
heal.classificationURI	N/A-Πληροφορική
heal.classificationURI	N/A-Μαθηματικά
heal.keywordURI	http://zbw.eu/stw/descriptor/19031-1
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh2002000760
heal.identifier.secondary	ISBN: 978-145031969-0
heal.identifier.secondary	DOI: 10.1145/2491845.2491892
heal.language	en
heal.access	campus
heal.recordProvider	Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Πληροφορικής Τ.Ε.	el
heal.publicationDate	2013-09
heal.bibliographicCitation	Tsoukalas, V. Th., Kaburlasos, V. G. and Skourlas, C. P. (2013). A granular, parametric KNN classifier. 17th Panhellenic Conference on Informatics(PCI) 2013. Thessaloniki, Greece. 19 - 21 September 2013. pp. 319-326. Available from: http://dl.acm.org/citation.cfm?doid=2491845.2491892.	en
heal.abstract	This work presents a granular K Nearest Neighbor, or grKNN for short, classifier in the metric lattice of Intervals' Numbers (INs). An IN here represents a population of numeric data samples. We detail how the grKNN classifier can be parameterized towards optimizing it. The capacity of a preliminary grKNN classifier is demonstrated, comparatively, in four benchmark classification problems. The far-reaching potential of the proposed classification scheme is discussed.	en
heal.sponsor	Αριστοτέλειο Πανεπιστήμιο	el
heal.sponsor	Αλεξάνδρειο Τ.Ε.Ι.	el
heal.sponsor	The University of Sheffield	el
heal.sponsor	Πανεπιστήμιο Μακεδονίας	el
heal.publisher	ACM	en
heal.fullTextAvailability	true
heal.conferenceName	17th Panhellenic Conference on Informatics	en
heal.conferenceItemType	full paper