Publication:
An Improvement to the k-Nearest Neighbor Classifier for ECG Database

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cris.author.scopus-author-id 55357649900
cris.author.scopus-author-id 57210707435
cris.author.scopus-author-id 57219027157
dc.contributor.author Jaafar H.
dc.contributor.author Ramli N.H.
dc.contributor.author Abdul Nasir A.S.
dc.date.accessioned 2025-01-13T06:44:14Z
dc.date.available 2025-01-13T06:44:14Z
dc.date.issued 2018-03-19
dc.description.abstract The k nearest neighbor (kNN) is a non-parametric classifier and has been widely used for pattern classification. However, in practice, the performance of kNN often tends to fail due to the lack of information on how the samples are distributed among them. Moreover, kNN is no longer optimal when the training samples are limited. Another problem observed in kNN is regarding the weighting issues in assigning the class label before classification. Thus, to solve these limitations, a new classifier called Mahalanobis fuzzy k-nearest centroid neighbor (MFkNCN) is proposed in this study. Here, a Mahalanobis distance is applied to avoid the imbalance of samples distribition. Then, a surrounding rule is employed to obtain the nearest centroid neighbor based on the distributions of training samples and its distance to the query point. Consequently, the fuzzy membership function is employed to assign the query point to the class label which is frequently represented by the nearest centroid neighbor Experimental studies from electrocardiogram (ECG) signal is applied in this study. The classification performances are evaluated in two experimental steps i.e. different values of k and different sizes of feature dimensions. Subsequently, a comparative study of kNN, kNCN, FkNN and MFkCNN classifier is conducted to evaluate the performances of the proposed classifier. The results show that the performance of MFkNCN consistently exceeds the kNN, kNCN and FkNN with the best classification rates of 96.5%.
dc.identifier.doi 10.1088/1757-899X/318/1/012046
dc.identifier.scopus 2-s2.0-85045626033
dc.identifier.uri https://hdl.handle.net/20.500.14170/11431
dc.relation.funding Universiti Malaysia Perlis
dc.relation.grantno 9001-00500
dc.relation.ispartof IOP Conference Series: Materials Science and Engineering
dc.relation.ispartofseries IOP Conference Series: Materials Science and Engineering
dc.relation.issn 17578981
dc.rights open access
dc.title An Improvement to the k-Nearest Neighbor Classifier for ECG Database
dc.type Conference Proceeding
dspace.entity.type Publication
oaire.citation.issue 1
oaire.citation.volume 318
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.affiliation.orgunit Universiti Malaysia Perlis
oairecerif.citation.number 012046
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
person.identifier.scopus-author-id 55357649900
person.identifier.scopus-author-id 57210707435
person.identifier.scopus-author-id 57219027157
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