dc.contributor.author | Ali, Shahid | |
dc.contributor.author | Dacey, Simon | |
dc.date.accessioned | 2018-08-02T20:29:59Z | |
dc.date.available | 2018-08-02T20:29:59Z | |
dc.date.issued | 2017 | |
dc.identifier.issn | 2230-9608 | |
dc.identifier.issn | 2231-007X | |
dc.identifier.uri | https://hdl.handle.net/10652/4341 | |
dc.description.abstract | Environmental 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.iso | en | en_NZ |
dc.publisher | AIRCC Publishing Corporation | en_NZ |
dc.relation.uri | http://aircconline.com/ijdkp/V7N6/7617ijdkp02.pdf | en_NZ |
dc.rights | All rights reserved | en_NZ |
dc.subject | Auckland (N.Z.) | en_NZ |
dc.subject | support vector machine (SVM) | en_NZ |
dc.subject | SVM | en_NZ |
dc.subject | Online Scalable SVM Ensemble Learning Method (OSSELM) | en_NZ |
dc.subject | ensemble learning | en_NZ |
dc.subject | air pollution analysis | en_NZ |
dc.subject | spatio-temporal | en_NZ |
dc.subject | aggregation (machine learning) | en_NZ |
dc.subject | scalable | |
dc.subject | New Zealand | en_NZ |
dc.title | Online scalable SVM ensemble learning method (OSSELM) for spatio-temporal air pollution analysis | en_NZ |
dc.type | Journal Article | en_NZ |
dc.date.updated | 2018-06-12T14:30:04Z | |
dc.rights.holder | Authors | en_NZ |
dc.identifier.doi | doi:10.5121/ijdkp.2017.7602 | en_NZ |
dc.subject.marsden | 170203 Knowledge Representation and Machine Learning | en_NZ |
dc.identifier.bibliographicCitation | Ali, 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.2017.7602 | en_NZ |
unitec.publication.spage | 21 | en_NZ |
unitec.publication.lpage | 38 | en_NZ |
unitec.publication.volume | 7 | en_NZ |
unitec.publication.issue | 5/6 | en_NZ |
unitec.publication.title | International Journal of Data Mining & Knowledge Management Process | en_NZ |
unitec.peerreviewed | yes | en_NZ |
dc.contributor.affiliation | Unitec Institute of Technology | en_NZ |
unitec.identifier.roms | 61552 | en_NZ |
unitec.publication.place | Chennai, Tamil Nadu, India | en_NZ |
unitec.institution.studyarea | Computing | |