Tree based classification methods for occupancy detection

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dc.contributor.authorKoklu M.
dc.contributor.authorTutuncu K.
dc.date.accessioned2020-03-26T20:20:03Z
dc.date.available2020-03-26T20:20:03Z
dc.date.issued2019
dc.departmentSelçuk Üniversitesien_US
dc.descriptionInternational Scientific and Practical Conference Engineering Systems 2019, ISPCES 2019 -- 4 April 2019 through 5 April 2019 -- 155575en_US
dc.description.abstractLatest smart buildings are not only be intelligent to allow occupant to control the light, heating, cooling, gas and other systems but also focuses on occupancy detection since accurate occupancy detection can result in saving energy up to 42% as can be seen in literature. For this aim, different autonomous systems including sensors, actuators, microcontroller and etc. are at the development phase for smart buildings. At this point, determination of classification methods to detect the occupancy together with hardware plays crucial role. Having done in this study 3 different classification methods that is based on Machine Learning Methods were applied on benchmark dataset named Occupancy by UCI Machine Learning Repository, 2016. The classifiers are Random Forest, Decision Tree and Bagging. They were chosen by following two principals. First one is to have classifier methods that were not use in literature for benchmark dataset and the second one almost never usage of tree based classifiers in the literature. The Occupancy dataset consists of light, temperature, humidity, CO2 and occupancy. It has been seen that the highest accuracy or prediction ratio was obtained as 99, 368% by Decision Tree method namely; Random Forest. This result was compared with the results of the studies on the same benchmark dataset. It has been seen that it is the second best accuracy ratio after Fuzzy Granulation (Fgf) method among 16 different Machine Learning Based classification methods. Additionally, Decision Tree and Bagging had the accuracy ratio of 99.222% and 99.207, respectively. These ratios are also higher than other methods used in literature but Fgf. Thus this study showed how decision tree can be promising for occupancy detection. © Published under licence by IOP Publishing Ltd.en_US
dc.description.sponsorshipFirat University Scientific Research Projects Management Uniten_US
dc.description.sponsorshipThis study has been supported by Scientific Research Project of Selcuk University.en_US
dc.identifier.doi10.1088/1757-899X/675/1/012032en_US
dc.identifier.issn1757-8981en_US
dc.identifier.issue1en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://dx.doi.org/10.1088/1757-899X/675/1/012032
dc.identifier.urihttps://hdl.handle.net/20.500.12395/38489
dc.identifier.volume675en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Physics Publishingen_US
dc.relation.ispartofIOP Conference Series: Materials Science and Engineeringen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectBaggingen_US
dc.subjectClassification methodsen_US
dc.subjectDecision treeen_US
dc.subjectOccupancy detectionen_US
dc.subjectRandom foresten_US
dc.titleTree based classification methods for occupancy detectionen_US
dc.typeConference Objecten_US

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