Abstract
Inconsistency in the electronic nose data set may appear due to noise that originates from various sources like electrical equipments, measuring instruments and some times the process itself. The presence of high noise leads to produce data that are of conflicting decision and thus encounters misleading or biased results. Also the performance of the electronic nose depends upon the number of relevant, irredundant features present in the data set. In an electronic nose the features correspond to the sensor array. While deploying an electronic nose for a specific application, it is observed that some of the features (sensors response) may not be required rather than only a subset of the sensor array contributes to the decision, which implies the optimization of sensor array is also important. To obtain a consistent precise data set both the conflicting data and irrelevant features must be removed. The rough set theory proposed by Z. Pawlak, is capable of dealing with such an imprecise, inconsistent data set and in this paper, the rough-set based algorithm has been applied to remove the conflicting training patterns and optimize the sensor array in an electronic nose instrument used for sensing aroma of black tea samples.
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Bag, A.K., Tudu, B., Bhattacharyya, N., Bandyopadhyay, R. (2013). Removal of Inconsistent Training Data in Electronic Nose Using Rough Set. In: Meghanathan, N., Nagamalai, D., Chaki, N. (eds) Advances in Computing and Information Technology. Advances in Intelligent Systems and Computing, vol 177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31552-7_13
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