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dc.contributor.authorAli, Shahid
dc.contributor.authorDacey, Simon
dc.date.accessioned2018-08-02T20:49:53Z
dc.date.available2018-08-02T20:49:53Z
dc.date.issued2017
dc.identifier.issn2230-9608
dc.identifier.issn2231-007X
dc.identifier.urihttp://hdl.handle.net/10652/4342
dc.description.abstractIncomplete data is present in many study contents. This incomplete or uncollected data information is named as missing data (values), and considered as vital problem for various researchers. Even this missing data problem is faced more in air pollution monitoring stations, where data is collected from multiple monitoring stations widespread across various locations. In literature, various imputation methods for missing data are proposed, however, in this research we considered only existing imputation methods for missing data and recorded their performance in ensemble creation. The five existing imputation methods for missing data deployed in this research are series mean method, mean of nearby points, median of nearby points, linear trend at a point and linear interpolation respectively. Series mean (SM) method demonstrated comparatively better to other imputation methods with least mean absolute error and better performance accuracy for SVM ensemble creation on CO data set using bagging and boosting algorithms.en_NZ
dc.language.isoenen_NZ
dc.publisherAIRCC Publishing Corporationen_NZ
dc.relation.urihttp://aircconline.com/ijdkp/V7N6/7617ijdkp06.pdfen_NZ
dc.subjectmissing data problemen_NZ
dc.subjectensemble learningen_NZ
dc.subjectimputation methodsen_NZ
dc.subjectseries mean (SM) methoden_NZ
dc.subjectsupport vector machine (SVM)en_NZ
dc.subjectbootstrap aggregating (meta-algorithm)en_NZ
dc.subjectbagging (meta-algorithm)en_NZ
dc.subjectboosting (meta-algorithm)en_NZ
dc.subjectaggregation (machine learning)en_NZ
dc.subjectair pollution analysisen_NZ
dc.subjectSVMen_NZ
dc.titleTechnical review : performance of existing imputation methods for missing data in SVM ensemble creationen_NZ
dc.typeJournal Articleen_NZ
dc.date.updated2018-06-12T14:30:06Z
dc.rights.holderAuthorsen_NZ
dc.identifier.doidoi:10.5121/ijdkp.2017.7606en_NZ
dc.subject.marsden170203 Knowledge Representation and Machine Learningen_NZ
dc.identifier.bibliographicCitationAli, S., & Dacey, S. (2017). Technical Review: Performance of Existing Imputation Methods for Missing Data in SVM Ensemble Creation. International Journal of Data Mining & Knowledge Management Process (IJDKP), 7(6), 75-91. doi:10.5121/ijdkp.2017.7606en_NZ
unitec.publication.spage75en_NZ
unitec.publication.lpage91en_NZ
unitec.publication.volume7en_NZ
unitec.publication.issue6en_NZ
unitec.publication.titleInternational Journal of Data Mining & Knowledge Management Process (IJDKP)en_NZ
unitec.peerreviewedyesen_NZ
unitec.identifier.roms61553en_NZ
unitec.publication.placeChennai, Tamil Nadu, Indiaen_NZ


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