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dc.contributor.authorAli, Shahid
dc.contributor.authorDacey, Simon
dc.date.accessioned2018-08-02T20:29:59Z
dc.date.available2018-08-02T20:29:59Z
dc.date.issued2017
dc.identifier.issn2230-9608
dc.identifier.issn2231-007X
dc.identifier.urihttps://hdl.handle.net/10652/4341
dc.description.abstractEnvironmental air pollution studies fail to consider the fact that air pollution is a spatio-temporal problem. The volume and complexity of the data have created the need to explore various machine learning models, however, those models have advantages and disadvantages when applied to regional air pollution analysis, furthermore, most environmental problems are global distribution problems. This research addressed spatio-temporal problem using decentralized computational technique named Online Scalable SVM Ensemble Learning Method (OSSELM). Evaluation criteria for computational air pollution analysis includes: accuracy, real time & prediction, spatio-temporal and decentralised analysis, we assert that these criteria can be improved using the proposed OSSELM. Special consideration is given to distributed ensemble to resolve spatio-temporal data collection problem (i.e. the data collected from multiple monitoring stations dispersed over a geographical location). Moreover, the experimental results demonstrated that the proposed OSSELM produced impressive results compare to SVM ensemble for air pollution analysis in Auckland region.en_NZ
dc.language.isoenen_NZ
dc.publisherAIRCC Publishing Corporationen_NZ
dc.relation.urihttp://aircconline.com/ijdkp/V7N6/7617ijdkp02.pdfen_NZ
dc.subjectAuckland, New Zealanden_NZ
dc.subjectsupport vector machine (SVM)en_NZ
dc.subjectSVMen_NZ
dc.subjectOnline Scalable SVM Ensemble Learning Method (OSSELM)en_NZ
dc.subjectensemble learningen_NZ
dc.subjectair pollution analysisen_NZ
dc.subjectspatio-temporalen_NZ
dc.subjectaggregationen_NZ
dc.subjectscalable
dc.titleOnline scalable SVM ensemble learning method (OSSELM) for spatio-temporal air pollution analysisen_NZ
dc.typeJournal Articleen_NZ
dc.date.updated2018-06-12T14:30:04Z
dc.rights.holderAuthorsen_NZ
dc.identifier.doidoi:10.5121/ijdkp.2017.7602en_NZ
dc.subject.marsden170203 Knowledge Representation and Machine Learningen_NZ
dc.identifier.bibliographicCitationAli, S., & Dacey, S. (2017). Online Scalable SVM Ensemble Learning Method (OSSELM) for Spatio-Temporal Air Pollution Analysis. International Journal of Data Mining & Knowledge Management Process, 7, 21-38. doi:10.5121/ijdkp.2017.7602en_NZ
unitec.publication.spage21en_NZ
unitec.publication.lpage38en_NZ
unitec.publication.volume7en_NZ
unitec.publication.issue5/6en_NZ
unitec.publication.titleInternational Journal of Data Mining & Knowledge Management Processen_NZ
unitec.peerreviewedyesen_NZ
unitec.identifier.roms61552en_NZ
unitec.publication.placeChennai, Tamil Nadu, Indiaen_NZ


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