Abstract
| Language | Undefined |
|---|---|
| Title of host publication | Proceedings of the Sixth International Conference on MObile Ubiquitous Computing, Systems, Services and Technologies, UBICOMM 2012 |
| Place of Publication | USA |
| Publisher | Xpert Publishing Services |
| Pages | 71-76 |
| Number of pages | 6 |
| ISBN (Print) | 978-1-61208-236-3 |
| State | Published - 2012 |
Publication series
| Name | |
|---|---|
| Publisher | Xpert Publishing Services |
Keywords
- EWI-22478
- METIS-289772
- IR-82154
Cite this
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Using Unsupervised Learning to Improve the Naive Bayes Classifier for Wireless Sensor Networks. / Zwartjes, G.J.; Havinga, Paul J.M.; Smit, Gerardus Johannes Maria; Hurink, Johann L.
Proceedings of the Sixth International Conference on MObile Ubiquitous Computing, Systems, Services and Technologies, UBICOMM 2012. USA : Xpert Publishing Services, 2012. p. 71-76.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Using Unsupervised Learning to Improve the Naive Bayes Classifier for Wireless Sensor Networks
AU - Zwartjes,G.J.
AU - Havinga,Paul J.M.
AU - Smit,Gerardus Johannes Maria
AU - Hurink,Johann L.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - EWI-22478
KW - METIS-289772
KW - IR-82154
M3 - Conference contribution
SN - 978-1-61208-236-3
SP - 71
EP - 76
BT - Proceedings of the Sixth International Conference on MObile Ubiquitous Computing, Systems, Services and Technologies, UBICOMM 2012
PB - Xpert Publishing Services
CY - USA
ER -