A Machine Learning Approach to Indoor Occupancy Detection Using Non-Intrusive Environmental Sensor Data-Reference-Cited by-同舟云学术

A Machine Learning Approach to Indoor Occupancy Detection Using Non-Intrusive Environmental Sensor Data

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 3rd International Conference on Big Data and Internet of Things - BDIOT 2019
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Author:

Zemouri Sofiane¹,Gkoufas Yiannis²,Murphy John¹

Affiliation:

1. Performance Engineering Lab, University College Dublin, Dublin, Ireland

2. IBM Research Ireland, IBM, Dublin, Ireland

Publisher

ACM Press

Reference8 articles.

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2. J. Huang and C. X. Ling. Using auc and accuracy in evaluating learning algorithms. IEEE Transactions on knowledge and Data Engineering, 17(3):299--310, 2005.

3. H. M. Khoury and V. R. Kamat. Evaluation of position tracking technologies for user localization in indoor construction environments. Automation in Construction, 18(4):444--457, 2009.

4. K. P. Lam, M. Hoynck, R. Zhang, B. Andrews, Y.-S. Chiou, B. Dong, D. Benitez, et al. Information-theoretic environmental features selection for occupancy detection in open offices. In Eleventh International IBPSA Conference, pages 27--30. Citeseer, 2009.

5. N. Li, G. Calis, and B. Becerik-Gerber. Measuring and monitoring occupancy with an rfid based system for demand-driven hvac operations. Automation in construction, 24:89--99, 2012.

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