A novel fusion approach based on induced ordered weighted averaging operators for chemometric data analysis

Journal of Chemometrics

Volumn 27, Issue 12, 2013, Pages 447-456

  Alhichri, Haikel ^a   Bazi, Yakoub ^a   Alajlan, Naif ^a   Melgani, Farid ^b   Malek, Salim ^a   Yager, Ronald R ^a,c  

^a KING SAUD UNIVERSITY   (Saudi Arabia)

^b UNIVERSITY OF TRENTO   (Italy)

^c MACHINE INTELLIGENCE INSTITUTE   (United States)

Author keywords

Extreme learning machine (ELM); Gaussian process regression (GPR); Induced ordered weighted averaging (IOWA) operators; Infrared spectroscopic data; Prioritized aggregation (PA)

Indexed keywords

BAYESIAN NETWORKS; EVOLUTIONARY ALGORITHMS; GAUSSIAN DISTRIBUTION; LEARNING SYSTEMS; MATHEMATICAL OPERATORS; OPTIMIZATION; SPECTROSCOPIC ANALYSIS; STATISTICAL METHODS;

EXTREME LEARNING MACHINE; GAUSSIAN PROCESS REGRESSION; INDUCED ORDERED WEIGHTED AVERAGING OPERATORS; INFRARED SPECTROSCOPIC; INFRARED SPECTROSCOPIC DATA; LEARNING MACHINES; PRIORITIZED AGGREGATION; SPECTROSCOPIC DATA;

GAUSSIAN NOISE (ELECTRONIC);

EID: 84889686095     PISSN: 08869383     EISSN: 1099128X     Source Type: Journal    
DOI: 10.1002/cem.2557     Document Type: Article

References (40)

1. Sekulic S, Ward HW, Brannegan D, Stanley E, Evans C, Sciavolino S, Hailey P, Aldridge P. On-line monitoring of powder blend homogeneity by near-infrared spectroscopy. Anal. Chem. 1996; 68: 509-513.
2. Blanco M, Coello J, Eustaquio A, Iturriaga H, Maspoch S. Analytical control of pharmaceutical production steps by near infrared reflectance spectroscopy. Anal. Chim. Acta 1999; 392(2-3): 237-246.
3. Ozaki Y, Cho R, Ikegaya K, Muraishi S, Kawauchi K. Potential of near-infrared Fourier transform Raman spectroscopy in food analysis. Appl. Spectrosc. 1992; 46: 1503-1507.
4. Blanco M, Coello J, Garcia Fraga JM, Iturriaga H, Maspoch S, Pagès J. Determination of finishing oils in acrylic fibers by near-infrared reflectance spectroscopy. Analyst 1999; 122; 777-781.
5. Wold S, Sjostrom M, Eriksson L. PLS regression a basic tool for chemometrics. Chemom. Intell. Lab. Syst. 2001; 58: 109-130.
6. Verikas A, Bacauskiene M. Using artificial neural networks for process and system modeling. Chemom. Intell. Lab. Syst. 2003; 67: 187-191.
7. Nicolaï BM, Theron KI, Lammertyn J. Kernel PLS regression on wavelet transformed NIR spectra for prediction of sugar content of apple. Chemom. Intell. Lab. Syst. 2007; 85: 243-252.
8. Li H, Liang Y, Xu Q. Support vector machines and its applications in chemistry. Chemom. Intell. Lab. Syst. 2009; 95: 188-198.
9. Devos O, Ruckebusch C, Durand A, Duponchel L, Huvenne JP. Support vector machines (SVM) in near infrared (NIR) spectroscopy: focus on parameters optimization and model interpretation. Chemom. Intell. Lab. Syst. 2009; 96: 27-33.
10. Cao DS, Liang YZ, Xu QS, Hu QN, Zhang LX. Exploring nonlinear relationships in chemical data using kernel based methods. Chemom. Intell. Lab. Syst. 2011; 107: 106-115.
11. Chen T, Morris J, Martin E. Gaussian process regression for multivariate spectroscopic regression. Chemom. Intell. Lab. Syst. 2007; 87: 59-71.
12. Peng J, Li L, Tang YY. Combination of activation functions in extreme learning machines for multivariate calibration. Chemom. Intell. Lab. Syst. 2013; 120: 53-78.
13. Rasmussen C, Williams CKI. Gaussian process for machine learning. The MIT Press: Cambridge, MA, 2006.
14. Pasolli E, Melgani F, Blanzieri E. Gaussian process regression for estimating chlorophyll concentration in subsurface waters from remote sensing data. IEEE Geosci. Remote Sens. Letters 2010; 7: 464-468.
15. Bazi Y, Melgani F, Alajlan N. Improved estimation of water chlorophyll concentration with semisupervised Gaussian process regression. IEEE Trans. Geosci. Remote Sens. 2012; 50: 988-997.
16. Pasolli E, Melgani F, Alajlan N, Bazi Y. Active learning methods for biophysical parameter estimation. IEEE Trans. Geosci. Remote Sens. 2012; 50: 988-997.
17. Huang GB, Chen L, Siew SK. Universal approximation using incremental constructive feedforward neural networks with random hidden nodes. IEEE Trans. Neural Netw. 2006; 17: 879-892.
18. Huang GB, Chen L. Extreme learning machine: theory and applications. Neurocomputing 2006; 70: 489-501.
19. Huang GB, Zhou H, Ding X, Zhang R. Extreme learning machine for regression and multiclass classification. IEEE Trans. Sys. Man Cybern.-Part B: Cybern. 2012; 42: 513-529.
20. Zhang MH, Xu QS, Massart DL. Boosting partial least squares. Anal. Chem. 2005; 77: 1423-1431.
21. Zhou YP, Jiang JH, Wu HL, Shen GL, Yu RQ, Ozaki Y. Dry film method with ytterbium as the internal standard for near infrared spectroscopic plasma glucose assay coupled with boosting support vector regression. J. Chemom. 2006; 20: 13-21.
22. Shinzawa H, Jiang JH, Ritthiruangdej P, Ozaki Y. Investigations of bagged kernel partial least squares (KPLS) and boosting KPLS with applications to near-infrared (NIR) spectra. J. Chemom. 2006; 20: 436-444.
23. Benoudjit N, Melgani F, Bouzgou H. Multiple regression systems for spectroscopic data analysis. Chemom. Intell. Lab. Syst. 2009; 95: 144-149.
24. Shao X, Bian X, Cai W. An improved boosting partial least squares method for near-infrared spectroscopic quantitative analysis. Anal. Chim. Acta 2010; 666: 32-37.
25. Niu D, Wang F, Zhang L, He D, Jia M. Neural network ensemble modeling for nosiheptide fermentation process based on partial least squares regression. Chemom. Intell. Lab. Syst. 2011; 105: 125-130.
26. Donald D, Coomans D, Everingham Y. Joint multiple adaptive wavelet regression ensembles. Chemom. Intell. Lab. Syst. 2011; 108: 133-141.
27. Das S, Suganthan PN. Differential evolution: a survey of state-of-the-art. IEEE Trans. Evol. Comput. 2011; 15: 4-31.
28. Yager RR, Filev DP. Induced ordered weighted averaging operators. IEEE Trans. Syst. Man Cybern. B 1999; 29: 141-150.
29. Yager RR. On ordered weighted averaging aggregation in multicriteria decision making. IEEE Trans. Syst. Man Cybern. 1988; 18: 183-190.
30. Douak F, Benoudjit N, Melgani F. A two stage regression approach for spectroscopic data analysis. Chemom. Intell. Lab. Syst. 2009; 96: 27-33.
31. Yager RR. On prioritized multiple criteria aggregation operators. IEEE Trans. on Syst. Man Cybern. Part B Cybern. 2012; 42: 1297-1305.
32. Yager RR, Kacprzyk J, Beliakov G. Recent developments in the order weighted averaging operators: theory and practice. Springer: Berlin, 2011.
33. Liu X, Yu S. On the stress function-based OWA determination method with optimization criteria. IEEE Trans. Syst. Man. Cybern. Part B: Cybernetics 2012; 42: 246-257.
34. Ahn BS. Programming-based OWA operators with weight quadratic objective function. IEEE Trans. Fuzzy Syst. 2012; 20: 986-992.
35. Fuller R, Majlender P. On obtaining minimal variability OWA operator weights. Fuzzy Set. Syst. 2003; 136: 203-215.
36. Datasets provided by Prof. Marc Meurens, Université catholique de Louvain, BNUT, meurens@bnut.ucl.ac.be. Wine and orange juice datasets available from: http://www.ucl.ac.be/mlg/.
37. Rossi F, Lendasse A, Francois D, Wertz V, Verleysen M. Mutual information for the selection of relevant variables in spectrometric nonlinear modelling. Chemom. Intell. Lab. Syst. 2006; 80: 215-226.
38. Krier C, Rossi F, François D, Verleysen M. A data-driven functional projection approach for the selection of feature ranges in spectra with ICA or cluster analysis. Chemom. Intell. Lab. Syst. 2008; 91: 43-53.
39. Tecator meat sample dataset. Available from: http://lib.stat.cmu.edu/datasets/tecator.
40. Rasmussen C.E, Williams K.I. Gaussian Process software. [Online]. Available: http://www.gaussianprocess.org/gpml/code/matlab/doc/.