Comparing the performance of different features in sensor arrays

Sensors and Actuators, B: Chemical

Volumn 123, Issue 1, 2007, Pages 437-443

  Pardo, M ^a   Sberveglieri, G ^a  

^a INFM   (Italy)

Author keywords

Data analysis; Electronic nose; Feature selection; Pattern recognition; Sensor array

Indexed keywords

ADSORPTION; DATA REDUCTION; DESORPTION; ERROR DETECTION; FEATURE EXTRACTION; INTEGRAL EQUATIONS; PHASE SPACE METHODS;

ELECTRONIC NOSE (EN); METAL OXIDE SENSORS; PHASE SPACE INTEGRALS; RESPONSE CURVES;

SENSOR ARRAYS;

EID: 34047144329     PISSN: 09254005     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.snb.2006.09.041     Document Type: Article

References (22)

1. Martinelli E., Falconi C., D'Amico A., and Di Natale C. Feature extraction of chemical sensors in phase space. Sens. Actuator B 95 (2003) 132
2. In: Subramanian D., Greiner R., and Pearl J. (Eds). Special issue on "The Relevance of Relevance". Artif. Intell. 97 (1997)
3. In: Guyon I., and Elisseeff A. (Eds). Special issue on "Variable Selection". J. Mach. Learn. Res. 3 (2003)
4. Webb A. Statistical Pattern Recognition. second ed. (2002), Wiley
5. Duda R.O., Hart P.E., and Stork D.G. Pattern Classification. second ed. (2001), John Wiley & Sons
6. Carmel L., Levy S., Lancet D., and Harel D. A feature extraction method for chemical sensors in electronic noses. Sens. Actuator B 93 (2003) 67-76
7. Guyon I., Weston J., Barnhill S., and Vapnik V. Gene selection for cancer classification using support vector machines. Mach. Learn. 46 (2002) 389-422
8. Nakamoto T., Sasaki S., Fukuda A., and Moriizumi T. Selection method of sensing membranes in odor sensing system. Sens. Mater. 4 (1992) 111
9. Eklov T., Martensson P., and Lundstrom I. Selection of variables for interpreting multivariate gas sensor data. Anal. Chim. Acta 381 (1999) 221-232
10. Pardo A., Marco S., Ortega A., Perera A., Sundic T., and Samitier J. Proceedings of the Conference International Symposium on Olfaction and Electronic Noses ISOEN. Institute of Physics Publishing, London (2000) 83-88
11. Pardo M., et al. Data analysis for a hybrid sensor array. Sens. Actuator B 106 (2005) 137-144
12. Jain A., and Zongker D. Feature selection: evaluation, application, and small sample performance. IEEE Trans. Pattern Anal. Mach. Intell. 19 (1997) 153-158
13. Pearce T.C., Shiffman S.S., Nagle H.T., and Gardner J.W. Handbook of Machine Olfaction (2002), Wiley
14. Ulmer H., Mitrovics J., Noetzel G., Wiemar U., and Gopel W. Odours and flavours identified with hybrid modular sensor systems. Sens. Actuator B 43 (1992) 24-33
15. Singh S., Hines E.L., and Gardner J.W. Fuzzy neural computing of coffee and tainted-water data from an electronic nose. Sens. Actuator B 30 (1996) 185-190
16. Gardner J.W., Shurmer H.V., and Tan T.T. Application of an artificial electronic nose to the discrimination of coffee. Sens. Actuator B 6 (1992) 71-75
17. Sberveglieri G. Recent developments in semiconducting thin-film gas sensors. Sens. Actuator B 23 (1995) 103
18. Pardo M., et al. Data preprocessing enhances the classification of different brands of Espresso coffee with an electronic nose. Sens. Actuator B 69 (2000) 397-403
19. Pardo M., and Sberveglieri G. Coffee analysis with an electronic nose. IEEE Trans. Instr. Meas. 51 (2002)
20. 835 electronic nose and data analysis for evaluating coffee ripening. Sens. Actuator B 110 (2005) 73-80
21. Pardo M., Sberveglieri G., and Gardner J.W. Benchmarking Feature Selection for E-Noses. 11th International Symposium on Olfaction & Electronic Nose. Barcelona (2005)
22. Pardo M., Sberveglieri G., and Gardner J.W. Feature selection for high dimensionality data in chemical sensing. 4th IEEE International Conference on Sensors. Irvine (2005)