Abstract
Sensor data stream mining methods have recently brought significant attention to smart homes research. Through the use of sliding windows on the streaming sensor data, activities can be recognized through the sensor events. However, it remains a challenge to attain real-time activity recognition from the online streaming sensor data. This paper proposes a new event classification method called Adaptive Cluster-Based Ensemble Learning of Streaming sensor data (ACBEstreaming). It contains desirable features such as adaptively windowing sensor events, detecting relevant sensor events using a time decay function, preserving past sensor information in its current window, and forming online clusters of streaming sensor data. The proposed approach improves the representation of streaming sensor-events, learns and recognizes activities in an on-line fashion. Experiments conducted using a real-world smart home dataset for activity recognition have achieved better results than the current approaches.
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Shahi, A., Sulaiman, M.N., Mustapha, N., Perumal, T.: Naive bayesian decision model for the interoperability of heterogeneous systems in an intelligent building environment. Automat. Construct. 54, 83–92 (2015)
Jurek, A., Bi, Y., Shengli, W., Nugent, C.D.: Clustering-based ensembles as an alternative to stacking. IEEE Trans. Knowl. Data Eng. 26(9), 2120–2137 (2014)
Gopalratnam, K., Cook, D.J.: Online sequential prediction via incremental parsing: the active LeZi algorithm. IEEE Intell. Syst. 22(1), 52–58 (2007)
Rashidi, P.: Stream sequence mining for human activity discovery. In: Sukthankar, G., Geib, C., Bui, H., Pynadath, D., Goldman, R.P. (eds.) Plan, Activity, and Intent Recognition. Elsevier (2014)
Rashidi, T., Cook, D.J.: Adapting to resident preferences in smart environments. In: Proceedings of the AAAI Workshop on Advances in Preference Handling (2008)
Krishnan, N.C., Cook, D.J.: Activity recognition on streaming sensor data. Pervasive Mob. Comput. 10, 138–154 (2014)
Rashid, M.M., Gondal, I., Kamruzzaman, J.: Regularly frequent patterns mining from sensor data stream. In: Lee, M., Hirose, A., Hou, Z.-G., Kil, R.M. (eds.) ICONIP 2013, Part II. LNCS, vol. 8227, pp. 417–424. Springer, Heidelberg (2013)
Huỳnh, T., Blanke, U., Schiele, B.: Scalable recognition of daily activities with wearable sensors. In: Hightower, J., Schiele, B., Strang, T. (eds.) LoCA 2007. LNCS, vol. 4718, pp. 50–67. Springer, Heidelberg (2007)
van Kasteren, T., Noulas, A., Englebienne, G., Kröse, B.: Accurate activity recognition in a home setting. In: Proceedings of the 10th International Conference on Ubiquitous Computing, UbiComp 2008, pp. 1–9. ACM, New York (2008)
Okeyo, G., Chen, L., Wang, H., Sterritt, R.: Dynamic sensor data segmentation for real-time knowledge-driven activity recognition. Pervasive Mob. Comput. 10, 155–172 (2014)
Melville, P., Mooney, R.J.: Creating diversity in ensembles using artificial data. Inf. Fusion 6(1), 99–111 (2005)
Zhou, Z.-H.: Ensemble Methods: Foundations and Algorithms, 1st edn. Chapman & Hall/CRC, Boca Raton (2012)
Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Measur. 20(1), 37–46 (1960)
Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: MOA: massive online analysis. J. Mach. Learning Res. 11, 1601–1604 (2010)
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Shahi, A., Woodford, B.J., Deng, J.D. (2015). Event Classification Using Adaptive Cluster-Based Ensemble Learning of Streaming Sensor Data. In: Pfahringer, B., Renz, J. (eds) AI 2015: Advances in Artificial Intelligence. AI 2015. Lecture Notes in Computer Science(), vol 9457. Springer, Cham. https://doi.org/10.1007/978-3-319-26350-2_45
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DOI: https://doi.org/10.1007/978-3-319-26350-2_45
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