Open AccessRUS Boost Tree Ensemble Classifiers for Occupancy Detection
Author(s) -
V Murugananthan,
Udaya Kumar Durairaj,
Udaya Durairaj
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b1048.0782s219
Subject(s) - occupancy , decision tree , classifier (uml) , post occupancy evaluation , ensemble learning , decision tree learning , computer science , humidity , artificial intelligence , machine learning , statistics , pattern recognition (psychology) , engineering , mathematics , geography , meteorology , architectural engineering
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%