@ARTICLE{Osowski_Stanisław_Mining_2017,
 author={Osowski, Stanisław and Siwek, Krzysztof},
 volume={vol. 24},
 number={No 1},
 journal={Metrology and Measurement Systems},
 pages={27-44},
 howpublished={online},
 year={2017},
 publisher={Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation},
 abstract={The paper analyses the distorted data of an electronic nose in recognizing the gasoline bio-based additives. Different tools of data mining, such as the methods of data clustering, principal component analysis, wavelet transformation, support vector machine and random forest of decision trees are applied. A special stress is put on the robustness of signal processing systems to the noise distorting the registered sensor signals. A special denoising procedure based on application of discrete wavelet transformation has been proposed. This procedure enables to reduce the error rate of recognition in a significant way. The numerical results of experiments devoted to the recognition of different blends of gasoline have shown the superiority of support vector machine in a noisy environment of measurement.},
 type={Artykuły / Articles},
 title={Mining Data of Noisy Signal Patterns in Recognition of Gasoline Bio-Based Additives using Electronic Nose},
 URL={http://ochroma.man.poznan.pl/Content/107350/PDF-MASTER/131.pdf},
 doi={10.1515/mms-2017-0015},
 keywords={Data Mining, electronic nose, gasoline blends, random forest, support vector machine, wavelet denoising},
}