Improving the classification accuracy in electronic noses using multi-dimensional combining (MDC)
作者： Hong Chen , R.A. Goubran , T. Mussivand
DOI: 10.1109/ICSENS.2004.1426233
关键词: Linear discriminant analysis 、 Dimensionality reduction 、 Feature extraction 、 Data mining 、 Pattern recognition 、 Multidimensional signal processing 、 Artificial intelligence 、 Pattern recognition (psychology) 、 Artificial neural network 、 Parametric statistics 、 Fuzzy logic 、 Computer science
摘要: Traditional pattern recognition (PARC) methods, used in electronic noses (e-noses) are either parametric (such as k-nearest neighbors, KNN, and linear discriminant analysis, LDA) or non-parametric artificial neural network fuzzy logic). Multi-dimensional combining (MDC) is proposed to combine the classification outputs of individual classifiers into a more robust accurate one. Two implementations find classifiers, one based on various feature extraction methods other dimension reduction with three means combining. Six household fragrances were sampled using Cyranose 320 e-nose device. The acquired data (600 measurements) was split two sets, training testing. Experiments conducted at concentrations sample smell, numbers numbers. Results show advantage MDC over traditional PARC under all conditions.
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researchgate.net 参考文章(9)
H. Troy Nagle, Susan S. Schiffman, Tim C. Pearce, Julian W. Gardner English, Handbook of Machine Olfaction: Electronic Nose Technology ,(2003)
R. Gutierrez-Osuna, H.T. Nagle, A method for evaluating data-preprocessing techniques for odour classification with an array of gas sensors systems man and cybernetics. ,vol. 29, pp. 626- 632 ,(1999) , 10.1109/3477.790446
M. Kermit, O. Tomic, Independent component analysis applied on gas sensor array measurement data IEEE Sensors Journal. ,vol. 3, pp. 218- 228 ,(2003) , 10.1109/JSEN.2002.807488
J. Brezmes, N. Canyellas, E. Llobet, X. Vilanova, X. Correig, Application of artificial neural networks to the design and implementation of electronic olfactory systems Proceedings of the 5th Seminar on Neural Network Applications in Electrical Engineering. NEUREL 2000 (IEEE Cat. No.00EX287). pp. 75- 80 ,(2000) , 10.1109/NEUREL.2000.902388
M. Pardo, G. Sberveglieri, Learning from data: a tutorial with emphasis on modern pattern recognition methods IEEE Sensors Journal. ,vol. 2, pp. 203- 217 ,(2002) , 10.1109/JSEN.2002.800686
G.E. Searle, J.W. Gardner, M.J. Chappell, K.R. Godfrey, M.J. Chapman, System identification of electronic nose data from cyanobacteria experiments IEEE Sensors Journal. ,vol. 2, pp. 218- 229 ,(2002) , 10.1109/JSEN.2002.800286
P. Wide, F. Winquist, P. Bergsten, E.M. Petriu, The human-based multi-sensor fusion method for artificial nose and tongue sensor data instrumentation and measurement technology conference. ,vol. 47, pp. 1072- 1077 ,(1998) , 10.1109/IMTC.1998.679844
L. Xu, A. Krzyzak, C.Y. Suen, Methods of combining multiple classifiers and their applications to handwriting recognition systems man and cybernetics. ,vol. 22, pp. 418- 435 ,(1992) , 10.1109/21.155943
David G. Stork, Richard O. Duda, Peter E. Hart, Pattern Classification ,(1973)