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Olfactory Classification via Interpoint Distance Analysis
April 2001 (vol. 23 no. 4)
pp. 404-413

Abstract—Detection of the presence of a single prespecified chemical analyte at low concentration in complex backgrounds is a difficult application for chemical sensors. This article considers a database of artificial nose observations designed specifically to allow for the investigation of chemical sensor data analysis performance on the problem of trichloroethylene (TCE) detection. We consider an approach to this application which uses an ensemble of subsample classifiers based on interpoint distances. Experimental results are presented indicating that our nonparametric methodology is a useful tool in olfactory classification.

[1] P.J. Bickel and K.A. Doksum, Mathematical Statistics: Basic Ideas and Selected Topics. Oakland, Calif.: Holden-Day, 1977.
[2] L. Breiman, “Bagging Predictors,” Machine Learning, vol. 24, pp. 123-140, 1996.
[3] L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone, Classification and Regression Trees. London: Chapman&Hall, 1984
[4] T.M. Cover and P.E. Hart, “Nearest Neighbor Pattern Classification,” IEEE Trans. Information Theory, vol. 13, pp. 21-27, 1967.
[5] H.A. David, Order Statistics, New York: Wiley, 1970.
[6] L. Devroye, L. Gyorfi, and G. Lugosi, A Probabilistic Theory of Pattern Recognition, New York: Springer, 1996.
[7] T.A. Dickinson, J. White, J.S. Kauer, D.R. Walt, “A Chemical-Detecting System Based on a Cross-Reactive Optical Sensor Array,” Nature, vol. 382, pp. 697-700, 1996.
[8] T.G. Dietterich, “Machine Learning Research: Four Current Directions,” AI Magazine, vol. 18, no. 4, pp. 97-136, 1997.
[9] J.H. Friedman, “Another Approach to Polychotomous Classification,” technical report, Stanford Univ., 1996. (unpublished).
[10] R. Guitierrez-Osuna, “Olfactory Signal Processing and Pattern Recognition,” IEEE Spectrum, vol. 35, no. 9, p. 28, Sept. 1998.
[11] L.K. Hansen and P. Salamon, “Neural Network Ensembles,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 10, pp. 993-1001, Oct. 1990.
[12] T.J. Hastie and R.J. Tibshirani, Generalized Additive Models, London: Chapman and Hall, 1990.
[13] T. Hastie and R. Tibshirani, “Discriminant Adaptive Nearest Neighbor Classification,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 6, pp. 607-615, June 1996.
[14] T. Hastie and R. Tibshirani, “Classification by Pairwise Coupling,” Ann. Statistics, vol. 26, no. 2, pp. 451-471, 1998.
[15] M. Hellman, “The Nearest Neighbor Classification Rule with a Reject Option,” IEEE Trans. Systems Science and Cybernetics, vol. 6, pp. 179-185, 1970.
[16] T.K. Ho, J.J. Hull, and S.N. Srihari, “Decision Combination in Multiple Classifiers Systems,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, no. 1, pp. 66-75, Jan. 1994.
[17] IEEE Spectrum, special issue on Electronic Noses, vol. 35, no. 9, pp. 22-38 Sept. 1998.
[18] T. Joachims, "Making Large-Scale SVM Learning Practical," to be published in Advances in Kernel Methods—Support Vector Learning, MIT Press, 1998.
[19] G. Kaplan and R. Braham, “Special Report: A Nose is a Nose is a Nose?” IEEE Spectrum, vol. 35, no. 9, p. 22, Sept. 1998.
[20] J. Kittler, M. Hatef, R. Duin, and J. Matas, “On Combining Classifiers,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 3, pp. 226-239, Mar. 1998.
[21] S.R. Kulkarni, G. Lugosi, and S. Venkatesh, “Learning Pattern Classification—A Survey,” IEEE Trans. Information Theory, vol. 44, no. 6, pp. 2178-2206, 1998.
[22] J.-F. Maa, D.K. Pearl, and R. Bartoszynski, “Reducing Multidimensional Two-Sample Data to One-Dimensional Interpoint Comparisons,” Ann. Statistics, vol. 24, pp. 1069-1074, 1996.
[23] H.B. Mann and D.R. Whitney, “On a Test Whether One of Two Random Variables is Stochastically Larger than the Other,” Ann. Math. Statistics, vol. 18, pp. 50-60, 1947.
[24] IEEE Spectrum, special issue on Electronic Noses, vol. 35, no. 9, pp. 22-38 Sept. 1998.
[25] C.E. Priebe and L.J. Cowen, “A Generalized Wilcoxon Mann-Whitney Statistic,” Comm. Statistics: Theory and Methods, vol. 28, no. 12, pp. 2871-2878, 1999.
[26] C.E. Priebe, D.J. Marchette, and J.L. Solka, “On the Selection of Distance for a High-Dimensional Classification Problem,” Proc. Statistical Computing Section of the Am. Statistical Assoc., 2000.
[27] J.O. Ramsay and B.W. Silverman, Functional Data Analysis. New York: Springer, 1997.
[28] B.D. Ripley, Pattern Recognition and Neural Networks, Cambridge Mass.: Cambridge Univ. Press, 1996.
[29] Science, special issue on Olfaction, vol. 286, no. 5540, pp. 703-728, Oct. 1999.
[30] D.B. Skalak, “Prototype Selection for Composite Nearest Neighbor Classifiers,” PhD Thesis, Dept. of Computer Science, Univ. of Massachusetts, Amherst, 1997.
[31] V.N. Vapnik, Statistical Learning Theory, John Wiley&Sons, 1998.
[32] M.P. Wand and M.C. Jones, Kernel Smoothing. London: Chapman&Hall, 1995.
[33] J. White, J.S. Kauer, T.A. Dickinson, and D.R. Walt, “Rapid Analyte Recognition in a Device Based on Optical Sensors and the Olfactory System,” Analytical Chemisty, pp. 2191-2202, vol. 68, 1996.
[34] F. Wilcoxon, “Individual Comparisons by Ranking Methods,” Biometrics, vol. 1, pp. 80-83, 1945.
[35] J. Xie and C.E. Priebe, “Generalizing the Mann-Whitney-Wilcoxon Statistic,” J. Nonparametric Statistics, vol. 12, pp. 661-682, 2000.

Index Terms:
Ensemble classifiers, combining classifiers, nonparametric, nearest-neighbor, interpoint distance, rank statistic, subsample statistic, functional data, artificial nose, electronic nose, analytical chemistry, chemometrics.
Citation:
Carey E. Priebe, "Olfactory Classification via Interpoint Distance Analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 4, pp. 404-413, April 2001, doi:10.1109/34.917575
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