On the Effects of Input Unreliability on Classifion Algorithms


Zwartjes, Ardjan and Bahrepour, Majid and Havinga, Paul J.M. and Hurink, Johann L. and Smit, Gerard J.M. (2011) On the Effects of Input Unreliability on Classifion Algorithms. In: 8th International ICST Conference on Mobile and Ubiquitous Systems, Mobiquitous 2011, 6-9 December 2011, Copenhagen, Denmark.

[img] PDF
Restricted to UT campus only
: Request a copy
Abstract: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 Item
Electrical Engineering, Mathematics and Computer Science (EEMCS)
Research Group:
Link to this item:http://purl.utwente.nl/publications/79624
Conference URL:http://mobiquitous.org/2011/
Export this item as:BibTeX
HTML Citation
Reference Manager


Repository Staff Only: item control page

Metis ID: 285058