Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/578543
Full metadata record
DC FieldValueLanguage
dc.contributor.authorNur Atikah Arbain (UTEM)
dc.contributor.authorMohd Sanusi Azmi (UTEM)
dc.contributor.authorSharifah Sakinah Syed Ahmad (UTEM)
dc.contributor.authorAzah Kamilah Muda (UTEM)
dc.contributor.authorIntan Ermahami A. Jalil (UTEM)
dc.contributor.authorKing Ming Tiang (UTEM)
dc.date.accessioned2023-11-06T03:03:23Z-
dc.date.available2023-11-06T03:03:23Z-
dc.date.issued2017-06
dc.identifier.issn0128-7680
dc.identifier.otherukmvital:116009
dc.identifier.urihttps://ptsldigital.ukm.my/jspui/handle/123456789/578543-
dc.descriptionIn recent years, many classification models have been developed and applied to increase their accuracy. The concept of distance between two samples or two variables is a fundamental concept in multivariate analysis. This paper proposed a tool that used different similarity distance approaches with ranking method based on Mean Average Precision (MAP). In this study, several similarity distance methods were used, such as Euclidean, Manhattan, Chebyshev, Sorenson and Cosine. The most suitable distance measure was based on the smallest value of distance between the samples. However, the real solution showed that the results were not accurate as and thus, MAP was considered the best approach to overcome current limitations.
dc.language.isoen
dc.publisherUniversiti Putra Malaysia Press
dc.relation.haspartPertanika Journals
dc.relation.urihttp://www.pertanika.upm.edu.my/regular_issues.php?jtype=2&journal=JST-25-S-6
dc.rightsUKM
dc.subjectAccuracy
dc.subjectMean average precision
dc.subjectRanking
dc.subjectSimilarity distance
dc.titleDynamic similarity distance with mean average precision tool
dc.typeJournal Article
dc.format.volume25
dc.format.pages11-18
dc.format.issueSpecial Issue
Appears in Collections:Journal Content Pages/ Kandungan Halaman Jurnal

Files in This Item:
File Description SizeFormat 
ukmvital_116009+Source01+Source010.PDF545.1 kBAdobe PDFThumbnail
View/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.