Optimization of sensor array in electronic nose: A rough set-based approach

IEEE Sensors Journal

Volumn 11, Issue 11, 2011, Pages 3001-3008

  Bag, Anil Kumar ^a   Tudu, Bipan ^b   Roy, Jayashri ^b   Bhattacharyya, Nabarun ^c   Bandyopadhyay, Rajib ^b  

^a FUTURE INSTITUTE OF ENGINEERING AND MANAGEMENT   (India)

^b JADAVPUR UNIVERSITY   (India)

^c CENTRE FOR DEVELOPMENT OF ADVANCED COMPUTING   (India)

Author keywords

Black tea; electronic nose; reduct; rough set; sensor array

Indexed keywords

BLACK TEA; CLASSIFICATION ACCURACY; DATA SETS; ELECTRONIC NOSE; MATHEMATICAL TOOLS; OPTIMIZATION OF SENSOR ARRAYS; REDUCT; REDUNDANT SENSORS; ROUGH SET; TIME OPTIMIZATION;

ARTIFICIAL ORGANS; CLASSIFICATION (OF INFORMATION); OPTIMIZATION; SENSOR ARRAYS;

ROUGH SET THEORY;

EID: 80055030513     PISSN: 1530437X     EISSN: None     Source Type: Journal    
DOI: 10.1109/JSEN.2011.2151186     Document Type: Article

References (37)

1. C. D. Natale, A. Macagnano, A. Mantini, F. Davide, A. D'Amnico, R. Paolesse, T. Boschi, M. Faccio, and G. Ferri, "Advances in food analysis by electronic nose," in Proc. IEEE Int. Symp. Ind. Electron., 1997, vol. 1, pp. 122-127.
2. Z. Pawlak, "Rough set theory and its applications," J. Telecom. Inform. Technol., vol. 3, pp. 7-10, 2002.
3. Z. Pawlak, "Some issues of rough sets," in Transactions on Rough Sets. Berlin, Germany: Springer-Verlag, 2004, vol. 3100, pp. 1-58.
4. Q. Shen and R. Jensen, "Rough sets, their extensions and applications," Int. J. f Autom. Comput., vol. 04, no. 3, pp. 217-228, Jul. 2007. (Pubitemid 47167348)
5. R. Li and Z. Wang, "Mining classification rules using rough sets and neutral networks," Eur. J. Oper. Res., vol. 157, pp. 439-448, 2004.
6. A. Butalia, M. Dhore, and G. Tewani, "Applications of rough sets in the field of data mining," in Proc. 1st Int. Conf. Emerging Trends Eng. Tech., Nagpur, India, Jul. 2008, pp. 498-503.
7. M. Ningler,G. Stockmanns, G. Schneider,O. Dressler, and E. F.Kochs, "Rough set-based classification of EEG-signals to detect intraoperative awareness: Comparison of fuzzy and crisp discretization of real value attributes," in Rough Sets and Current Trends in Computing. Berlin, Germany: Springer-Verlag, 2004, vol. 3066, pp. 825-834. (Pubitemid 38803607)
8. S. Mitra, M. Mitra, and B. B. Chaudhuri, "A rough-set-based inference engine for ECG classification," IEEE Trans. Instrum. Meas., vol. 55, pp. 2198-2206, 2006. (Pubitemid 46438204)
9. H. Al-Qaheri, A. E. Hassanien, and A. Abraham, "Discovering stock price prediction rules using rough sets," Neural Network World, vol. 18, no. 3, pp. 181-189, 2008.
10. J. Y. Shyng, F. K. Wang, G. H. Tzeng, and K. S. Wu, "Rough set theory in analyzing the attributes of combination values for the insurance market," Expt. Syst. Appl., vol. 32, pp. 56-64, 2007.
11. P. Maji and S. Paul, "Rough sets for selection of molecular descriptors to predict biological activity of molecules," IEEE Trans. Syst., Man, Cybern., Part C: Appl. Rev., vol. 40, no. 6, pp. 639-648, May 2010.
12. N. Zhong and A. Skowron, "A rough set-based knowledge discovery process," Int. J. Appl. Math. Comput. Sci., vol. 11, pp. 603-619, 2001.
13. B. B. Qu and Y. S. Lu, "A hierarchy reduct algorithm for feature subset selection," in Proc. 3rd Int. Conf. Mach. Learning Cybernatic, Shanghai, Aug. 2004, vol. 2, pp. 1157-1161.
14. B. F. Momin, S. Mitra, and R. D. Gupta, "Reduct generation and classification of gene expression data," in Proc. Int. Conf. Hybrid Inf. Tech., Cheju Island, Korea, Nov. 2006, vol. 1, pp. 699-708. (Pubitemid 46604157)
15. P. Yang, J. Li, and Y. Huang, "An attribute reduction algorithm by rough set based on binary discernibility matrix," in Proc. 5th In. Conf. Fuzzy Syst. Knowl. Discovery, Oct. 2008, vol. 2, pp. 276-280.
16. D. M.Willson, K. Dunman, T. Roppel, and R. Kalim, "Rank extraction in tin oxide sensor arrays," Sens. Actuators B, vol. 6, pp. 199-210, 2000. (Pubitemid 30561133)
17. P. W. Carey, K. R. Beebe, B. R. Kowalaski, D. L. Illman, and T. Hirschfeld, "Selection of adsorbates for chemical sensor arrays by pattern-recognition," Anal. Chem., vol. 58, pp. 149-153, 1986. (Pubitemid 16502289)
18. B. G. Kermani, S. S. Schiffman, and H. T. Nagle, "A novel method for reducing the dimensionality in a sensor array," IEEE Trans. Instrum. Meas., vol. 47, no. 3, pp. 728-741, Jun. 1998. (Pubitemid 128738599)
19. K. T. Lau, J. Micklefield, and J. M. Slater, "The optimization of sorption sensor arrays for use in ambient conditions," Sens. Actuators B, vol. 50, pp. 69-79, 1998. (Pubitemid 128458033)
20. S. Benedetti, S. Mannino, A. G. Sabatini, and G. L. Marcazzan, "Electronic nose and neural network use for the classification of honey," Apidologie © INRA/DIB-AGIB/ EDP Sciences, vol. 35, pp. 1-6, 2004.
21. M. Pardo, L. G. Kwong, G. Sberveglieri, K. Brubaker, J. F. Schneider, W. R. Penrose, and J. R. Stetter, "Data analysis for a hybrid sensor array," Sens. Actuators B, vol. 106, pp. 136-143, 2005. (Pubitemid 40480235)
22. T. Elkov, P. Martensson, and I. Lundstrom, "Selection of variables for interpreting multivariate gas sensor data," Anal. Chim. Acta., vol. 381, pp. 221-232, 1999. (Pubitemid 29073299)
23. J. W. Gardner, P. Boilot, and E. L. Hines, "Enhancing electronic nose performance by sensor selection using a new integer-based genetic algorithm approach," Sens. Actuators B, vol. 106, pp. 114-121, 2005. (Pubitemid 40480232)
24. E. Phaisangittisagul, H. T. Nagle, and V. Areekul, "Intelligent method for sensor subset selection for machine olfaction," Sens. Actuators B: Chem., vol. 145, pp. 507-515, 2010.
25. E. Schaller, J. O. Bosset, and F. Escher, "'Electronic noses' and their application to food," LWT - Food Sci. Technol., vol. 31, no. 4, pp. 305-316, Apr. 1998. (Pubitemid 128348415)
26. J. W. Gardner, E. L. Hines, and M.Wilkinson, "Application of artificial neural networks to an electronic olfactory system," Meas. Sci. Technol., vol. 1, no. 5, pp. 446-451, 1990. (Pubitemid 20685071)
27. S. H. Nguyen and A. Skowron, "Quantization of real value attributes, Rough set and boolean reasoning approach," in Proc. 2nd Joint Annu. Conf. Inform. Sci., Wrightsville Beach, NC, 1995, pp. 34-37.
28. J. H. Dai and Y. X. Li, "Study on discretization based on rough set theory," in Proc. 1st Int. Conf. Mach. Learning Cybern., Beijing, Nov. 2002, pp. 1371-1373.
29. J. Li, P. Pattaraintakorn, and N. Cercone, "Rule evaluations, attributes, and rough sets: Extension and a case study," in Transactions on Rough Sets VI. Berlin, Germany: Springer-Verlag, 2007, pp. 152-171. (Pubitemid 351256441)
30. E. Kovacs and I. Ignat, "Reduct equivalent rule induction based on rough set theory," in Proc. IEEE 3rd Int. Conf. Intell. Comput. Commun. Process., 2007, pp. 9-15.
31. R. Dutta, E. L. Hines, J. W. Gardner, K. R. Kashwan, and M. Bhuyan, "Tea quality prediction using a tin oxide-based electronic nose: An artificial intelligence approach," Sens. Actuators B: Chem., vol. 94, pp. 228-237, 2003.
32. N. Bhattacharyya, R. Bandyopadhyay,M. Bhuyan, B. Tudu, D. Ghosh, and A. Jana, "Electronic nose for black tea classification and correlation of measurements with "Tea Taster" marks," IEEE Trans. Instrum. Meas., vol. 57, pp. 1313-1321, 2008.
33. M. E. Wall, A. Rechtsteiner, and L. M. Rocha, "Singular value decomposition and principal component analysis," in A Practical Approach to Microarray Data Analysis, D. P. Berrar, W. Dubitzky, and M. Granzow, Eds. Norwell, MA: Kluwer, 2003, ch. 5, pp. 91-109.
34. R. O. Duda, D. G. Stork, and P. E. Hart, Pattern classification, 2nd ed. New York: Wiley, 2001, p. 115.
35. S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd ed. Singapore: Pearson Education, Asia, 2001.
36. J. D. Rodriguez, A. Perez, and J. A. Lozano, "Sensitivity analysis of k-fold cross validation in prediction error estimation," IEEE Trans Pattern Anal. Mach. Intel., vol. 32, no. 3, pp. 569-575, Mar. 2010.
37. S. Singh, E. L. Hines, and J. W. Gardner, "Fuzzy neural computing of coffee and tainted-water data from an electronic nose," Sens. Actuators B, vol. 30, no. 3, pp. 185-190, Jan. 1996. (Pubitemid 126384089)