RUS Boost Tree Ensemble Classifiers for Occupancy Detection
V. Murugananthan1, Udaya Kumar Durairaj2

1V. Murugananthan, Lecturer, SEEMIT, Institute Technology Pertama, Mantin, Negeri Sembilan, Malaysia.
2Udaya Kumar Durairaj, Lecture, FOET, Lipnton University College, Mantin, Negeri Sembilan, Malaysia.
Manuscript received on 27 June 2019 | Revised Manuscript received on 15 July 2019 | Manuscript Published on 26 July 2019 | PP: 272-277 | Volume-8 Issue-2S2 July 2019 | Retrieval Number: B10480782S219/2019©BEIESP | DOI: 10.35940/ijrte.B1048.0782S219
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Abstract: In this research paper, various ensemble classifiers are used to predict occupancy status using samples of light, temperature, humidity, CO2 , humidity ratio sensor data. Occupancy detection will save energy making room for smart buildings in smart cities. It paves ways to decide on heating, ventilation, cooling and lighting. To achieve ‘white box’ output and facilitate explanatory interpretation, decision tree was employed, Several weak learner decision trees were melded to form RUSBoosted Tree ensemble classifier. On investigation of the results, it is seen that RUSBoostedTree Ensemble gives the highest accuracy rate of 99%.
Keywords: Occupancy Detection, Classification, Ensemble, Rusboosted Tree Ensemble Classifier, Sensor Data.
Scope of the Article: Classification