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dc.contributor.authorQi, Jin-Peng
dc.contributor.authorZhu, Ying
dc.contributor.authorZhang, Ping
dc.contributor.editorZheng, H
dc.contributor.editorCallejas, Z
dc.contributor.editorGriol, D
dc.contributor.editorWang, H
dc.contributor.editorHu, X
dc.contributor.editorSchmidt, H
dc.contributor.editorBaumbach, J
dc.contributor.editorDickerson, J
dc.contributor.editorZhang, L
dc.date.accessioned2019-07-04T12:37:18Z
dc.date.available2019-07-04T12:37:18Z
dc.date.issued2018
dc.identifier.isbn9781538654880
dc.identifier.issn2156-1125
dc.identifier.doi10.1109/BIBM.2018.8621440
dc.identifier.urihttp://hdl.handle.net/10072/384002
dc.description.abstractChange point detection (CPD) is to find the abrupt changes in a time series. Various computational algorithms have been developed for CPD. To compare the different CPD models, many performance metrics have been introduced to evaluate the algorithms. Each of the previous evaluation methods measures the different aspect of the methods. In this paper, a new weighted error distance (WED) method is proposed to evaluate the overall performance of a CPD model across multiple time series of different lengths. A concept of normalized error distance was introduced to allow comparison of the distances between an estimated change point position and the target change point among models that work on multiple time series. In this study, the WED metrics was applied on synthetic datasets with different sample sizes and variances to evaluate the different CPD models, including: Kolmogorov-Smirnov (KS), SSA and T algorithms. The test results showed the value of this WED method that contributes to the methodology for evaluating the performance of CPD models.
dc.languageEnglish
dc.publisherIEEE
dc.relation.ispartofconferencenameIEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Human Genomics
dc.relation.ispartofconferencetitlePROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM)
dc.relation.ispartofdatefrom2018-12-03
dc.relation.ispartofdateto2018-12-06
dc.relation.ispartoflocationMadrid, SPAIN
dc.relation.ispartofpagefrom1406
dc.relation.ispartofpageto1410
dc.subject.fieldofresearchBioinformatics
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchcode060102
dc.subject.fieldofresearchcode0801
dc.titleA WED Method for Evaluating the Performance of Change-Point Detection Algorithms
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dc.description.versionPost-print
gro.rights.copyright© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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gro.griffith.authorZhang, Ping


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