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Zwartjes, G.J. and Havinga, P.J.M. and Smit, G.J.M. and Hurink, J.L. (2012) Using Unsupervised Learning to Improve the Naive Bayes Classifier for Wireless Sensor Networks. In: Proceedings of the Sixth International Conference on MObile Ubiquitous Computing, Systems, Services and Technologies, UBICOMM 2012, 23-28 Sep 2012, Barcelona, Spain. pp. 71-76. Xpert Publishing Services. ISBN 978-1-61208-236-3
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Online processing is essential for many sensor network applications. Sensor nodes can sample far more data than what can practically be transmitted using state of the art sensor network radios. Online processing, however, is complicated due to limited resources of individual nodes. The naive Bayes classifier is an algorithm proven to be suitable for online classification on Wireless Sensor Networks. In this paper, we investigate a new technique to improve the naive Bayes classifier while maintaining sensor network compatibility. We propose the application of unsupervised learning techniques to enhance the probability density estimation needed for naive Bayes, thereby achieving the benefits of binning histogram probability density estimation without the related memory requirements. Using an offline experimental dataset, we demonstrate the possibility of matching the performance of the binning histogram approach within the constraints provided by Wireless Sensor Network hardware. We validate the feasibility of our approach using an implementation based on Arduino Nano hardware combined with NRF24L01+ radios.
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