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21380 On the Effects of Input Unreliability on Classifion Algorithms
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Zwartjes, G.J. and Bahrepour, M. and Havinga, P.J.M. and Hurink, J.L. and Smit, G.J.M. (2011) On the Effects of Input Unreliability on Classifion Algorithms. In: 8th International ICST Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services, MobiQuitous 2011, 6-9 Dec 2011, Copenhagen, Denmark. pp. 126-137. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST) 104. Springer Verlag. ISSN 1867-8211 ISBN 978-3-642-30972-4

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The abundance of data available on Wireless Sensor Networks makes online processing necessary. In industrial applications, for example, the correct operation of equipment can be the point of interest. The raw sampled data is of minor importance. Classification algorithms can be used to make state classifications based on the available data for devices such as industrial refrigerators.

The reliability through redundancy approach used in Wireless Sensor Networks complicates practical realizations of classification algorithms. Individual inputs are susceptible to multiple disturbances like hardware failure, communication failure and battery depletion. In order to demonstrate the effects of input failure on classification algorithms, we have compared three widely used algorithms in multiple error scenarios. The compared algorithms are Feed Forward Neural Networks, naive Bayes classifiers and decision trees.

Using a new experimental data-set, we show that the performance under error scenarios degrades less for the naive Bayes classifier than for the two other algorithms.

Item Type:Conference or Workshop Paper (Full Paper, Talk)
Research Group:EWI-PS: Pervasive Systems, EWI-CAES: Computer Architecture for Embedded Systems, EWI-DMMP: Discrete Mathematics and Mathematical Programming
Research Program:CTIT-WiSe: Wireless and Sensor Systems
Research Project:Real Time Enterprise
Additional Information:
ID Code:21380
Deposited On:02 February 2012
More Information:statisticsmetis

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