Publication:
Room occupancy detection using modified stacking

dc.contributor.authorPawalai Kraipeerapunen_US
dc.contributor.authorSomkid Amornsamankuren_US
dc.contributor.otherRamkhamhaeng Universityen_US
dc.contributor.otherMahidol Universityen_US
dc.date.accessioned2018-12-21T07:21:51Z
dc.date.accessioned2019-03-14T08:03:26Z
dc.date.available2018-12-21T07:21:51Z
dc.date.available2019-03-14T08:03:26Z
dc.date.issued2017-02-24en_US
dc.description.abstract© 2017 ACM. Occupancy detection is a binary classification task. However, in this paper, stacking for multiclass classification is applied to detect occupancy of a room. Neural network with duo outputs are combined with stacking. The outputs of stacking for multiclass classification are then integrated to get a binary classification. The occupancy detection dataset obtained from UCI Machine Learning Repository is used in the experiment. It is found that our proposed stacking technique provides better accuracy result than the traditional stacking for binary classification.en_US
dc.identifier.citationACM International Conference Proceeding Series. Vol.Part F128357, (2017), 162-166en_US
dc.identifier.doi10.1145/3055635.3056597en_US
dc.identifier.other2-s2.0-85024390194en_US
dc.identifier.urihttps://repository.li.mahidol.ac.th/handle/123456789/42393
dc.rightsMahidol Universityen_US
dc.rights.holderSCOPUSen_US
dc.source.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85024390194&origin=inwarden_US
dc.subjectComputer Scienceen_US
dc.titleRoom occupancy detection using modified stackingen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
mu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85024390194&origin=inwarden_US

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