Feature selection and analysis on correlated gas sensor data with recursive feature elimination

Sensors and Actuators, B: Chemical

Volumn 212, Issue , 2015, Pages 353-363

  Yan, Ke ^a   Zhang, David ^b  

^a TSINGHUA UNIVERSITY   (China)

^b HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

Breath analysis; Correlation bias; Feature ranking; Feature selection; SVM RFE; Transient feature

Indexed keywords

ALGORITHMS; GAS DETECTORS; SUPPORT VECTOR MACHINES;

BREATH ANALYSIS; CORRELATION BIAS; FEATURE RANKING; SVM-RFE; TRANSIENT FEATURES;

FEATURE EXTRACTION;

EID: 84923826827     PISSN: 09254005     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.snb.2015.02.025     Document Type: Article

References (35)

1. I. Guyon, and A. Elisseeff An introduction to variable and feature selection J. Mach. Learn. Res. 3 2003 1157 1182
2. Y. Saeys, I. Inza, and P. Larrañaga A review of feature selection techniques in bioinformatics Bioinformatics 23 2007 2507 2517
3. N. Paulsson, E. Larsson, and F. Winquist Extraction and selection of parameters for evaluation of breath alcohol measurement with an electronic nose Sens. Actuators A: Phys. 84 2000 187 197
4. E. Llobet, O. Gualdrón, M. Vinaixa, N. El-Barbri, J. Brezmes, X. Vilanova, B. Bouchikhi, R. Gomez, J. Carrasco, and X. Correig Efficient feature selection for mass spectrometry based electronic nose applications Chemom. Intell. Lab. 85 2007 253 261
5. O. Gualdrón, J. Brezmes, E. Llobet, A. Amari, X. Vilanova, B. Bouchikhi, and X. Correig Variable selection for support vector machine based multisensor systems Sens. Actuators B: Chem. 122 2007 259 268
6. M. Pardo, and G. Sberveglieri Random forests and nearest shrunken centroids for the classification of sensor array data Sens. Actuators B: Chem. 131 2008 93 99
7. J.H. Cho, and P.U. Kurup Decision tree approach for classification and dimensionality reduction of electronic nose data Sens. Actuators B: Chem. 160 2011 542 548
8. R. Kaur, R. Kumar, A. Gulati, C. Ghanshyam, P. Kapur, and A.P. Bhondekar Enhancing electronic nose performance: a novel feature selection approach using dynamic social impact theory and moving window time slicing for classification of Kangra orthodox black tea (Camellia sinensis (L.) o. kuntze) Sens. Actuators B: Chem. 166 2012 309 319
9. S. Marco, and A. Gutiérrez-Gálvez Signal and data processing for machine olfaction and chemical sensing: a review IEEE Sens. J. 12 2012 3189 3214
10. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik Gene selection for cancer classification using support vector machines Mach. Learn. 46 2002 389 422
11. A. Rakotomamonjy Variable selection using SVM based criteria J. Mach. Learn. Res. 3 2003 1357 1370
12. K.-B. Duan, J.C. Rajapakse, H. Wang, and F. Azuaje Multiple SVM-RFE for gene selection in cancer classification with expression data IEEE Trans. NanoBiosci. 4 2005 228 234
13. Y. Tang, Y.-Q. Zhang, and Z. Huang Development of two-stage SVM-RFE gene selection strategy for microarray expression data analysis IEEE ACM Trans. Comput. Biol. Bioinform. 4 2007 365 381
14. S. Yoon, and S. Kim Mutual information-based SVM-RFE for diagnostic classification of digitized mammograms Pattern Recogn. Lett. 30 2009 1489 1495
15. P.A. Mundra, and J.C. Rajapakse SvM-RFE with MRMR filter for gene selection IEEE Trans. NanoBiosci. 9 2010 31 37
16. L. Toloşi, and T. Lengauer Classification with correlated features: unreliability of feature ranking and solutions Bioinformatics 27 2011 1986 1994
17. A. D'Amico, C. Di Natale, R. Paolesse, A. Macagnano, E. Martinelli, G. Pennazza, M. Santonico, M. Bernabei, C. Roscioni, and G. Galluccio Olfactory systems for medical applications Sens. Actuators B: Chem. 130 2008 458 465
18. K. Yan, D. Zhang, D. Wu, H. Wei, and G. Lu Design of a breath analysis system for diabetes screening and blood glucose level prediction IEEE Trans. Biomed. Eng. 61 2014 2787 2795
19. D. Guo, D. Zhang, N. Li, L. Zhang, and J. Yang A novel breath analysis system based on electronic olfaction IEEE Trans. Biomed. Eng. 57 2010 2753 2763
20. C.J. Burges A tutorial on support vector machines for pattern recognition Data Min. Knowl. Discov. 2 1998 121 167
21. M.Y. Park, T. Hastie, and R. Tibshirani Averaged gene expressions for regression Biostatistics 8 2007 212 227
22. D.B. Sharma, H.D. Bondell, and H.H. Zhang Consistent group identification and variable selection in regression with correlated predictors J. Comput. Graph. Stat. 22 2013 319 340
23. A.P. Bhondekar, R. Kaur, R. Kumar, R. Vig, and P. Kapur A novel approach using dynamic social impact theory for optimization of impedance-tongue (itongue) Chemom. Intell. Lab. 109 2011 65 76
24. W. Awada, T.M. Khoshgoftaar, D. Dittman, R. Wald, and A. Napolitano A review of the stability of feature selection techniques for bioinformatics data 2012 IEEE 13th Intl. Conf. on Information Reuse and Integration (IRI) 2012 IEEE Las Vegas, USA 356 363
25. P. Somol, and J. Novovicova Evaluating stability and comparing output of feature selectors that optimize feature subset cardinality IEEE Trans. Pattern Anal. Mach. Intell. 32 2010 1921 1939
26. Y. Saeys, T. Abeel, and Y. Van de Peer Robust feature selection using ensemble feature selection techniques Machine Learning and Knowledge Discovery in Databases 2008 Springer 313 325
27. A. Kalousis, J. Prados, and M. Hilario Stability of feature selection algorithms: a study on high-dimensional spaces Knowl. Inf. Syst. 12 2007 95 116
28. T.D.C. Minh, D.R. Blake, and P.R. Galassetti The clinical potential of exhaled breath analysis for diabetes mellitus Diabetes Res. Clin. Pract. 97 2012 195 205
29. K. Yan, and D. Zhang Sensor evaluation in a breath analysis system 2014 International Conference on Medical Biometrics (ICMB) 2014 IEEE 35 40
30. A. Hierlemann, and R. Gutierrez-Osuna Higher-order chemical sensing Chem. Rev. 108 2008 563 613
31. H. Peng, F. Long, and C. Ding Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy IEEE Trans. Pattern Anal. Mach. Intell. 27 2005 1226 1238
32. S. Hosseini-Golgoo, and F. Hossein-Babaei Assessing the diagnostic information in the response patterns of a temperature-modulated tin oxide gas sensor Meas. Sci. Technol. 22 2011 035201
33. S. Zhang, C. Xie, M. Hu, H. Li, Z. Bai, and D. Zeng An entire feature extraction method of metal oxide gas sensors Sens. Actuators B: Chem. 132 2008 81 89
34. R. Gutierrez-Osuna, A. Gutierrez-Galvez, and N. Powar Transient response analysis for temperature-modulated chemoresistors Sens. Actuators B: Chem. 93 2003 57 66
35. E. Martinelli, C. Falconi, A. D'Amico, and C. Di Natale Feature extraction of chemical sensors in phase space Sens. Actuators B: Chem. 95 2003 132 139