Submitted by Cássia Santos (cassia.bcufg@gmail.com) on 2014-09-19T11:19:07Z
No. of bitstreams: 2
Dissertacao Daniel Vitor de Lucena.pdf: 708978 bytes, checksum: 466a21a76649073c30364b80f17037fc (MD5)
license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2014-09-19T11:25:02Z (GMT) No. of bitstreams: 2
Dissertacao Daniel Vitor de Lucena.pdf: 708978 bytes, checksum: 466a21a76649073c30364b80f17037fc (MD5)
license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2014-09-19T11:25:02Z (GMT). No. of bitstreams: 2
Dissertacao Daniel Vitor de Lucena.pdf: 708978 bytes, checksum: 466a21a76649073c30364b80f17037fc (MD5)
license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5)
Previous issue date: 2013-05-03 / This work proposes the use of multi-objective genetics algorithms NSGA-II and SPEA-II
on the variable selection in multivariate calibration problems. These algorithms are used
for selecting variables for a Multiple Linear Regression (MLR) by two conflicting objectives:
the prediction error and the used variables number in MLR. For the case study
are used wheat data obtained by NIR spectrometry with the objective for determining a
variable subgroup with information about protein concentration. The results of traditional
techniques of multivariate calibration as the Partial Least Square (PLS) and Successive
Projection Algorithm (SPA) for MLR are presents for comparisons. The obtained
results showed that the proposed approach obtained better results when compared with
a monoobjective evolutionary algorithm and with traditional techniques of multivariate
calibration. / Este trabalho propõe a utilização dos algoritmos genéticos multiobjetivo NSGA-II e
SPEA-II na seleção de variáveis em problemas de calibração multivariada. Esses algoritmos
são utilizados para selecionar variáveis para Regressão Linear Múltipla (MLR)
com dois objetivos conflitantes: o erro de predição e do número de variáveis utilizadas na
MLR. Para o estudo de caso são usado dados de trigo obtidos por espectrometria NIR com
o objetivo de determinar um subgrupo de variáveis com informações sobre a concentração
de proteína. Os resultados das técnicas tradicionais de calibração multivariada como dos
Mínimos Quadrados Parciais (PLS) e Algoritmo de Projeções Sucessivas (APS) para a
MLR estão presentes para comparações. Os resultados obtidos mostraram que a abordagem
proposta obteve melhores resultados quando comparado com um algoritmo evolutivo
monoobjetivo e com as técnicas tradicionais de calibração multivariada.
| Identifer | oai:union.ndltd.org:IBICT/oai:repositorio.bc.ufg.br:tede/3096 |
| Date | 03 May 2013 |
| Creators | Lucena, Daniel Vitor de |
| Contributors | Soares, Telma Woerle de Lima |
| Publisher | Universidade Federal de Goiás, Programa de Pós-graduação em Ciência da Computação (INF), UFG, Brasil, Instituto de Informática - INF (RG) |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/masterThesis |
| Format | application/pdf |
| Source | reponame:Biblioteca Digital de Teses e Dissertações da UFG, instname:Universidade Federal de Goiás, instacron:UFG |
| Rights | http://creativecommons.org/licenses/by-nc-nd/4.0/, info:eu-repo/semantics/openAccess |
| Relation | -3303550325223384799, 600, 600, 600, -7712266734633644768, -862078257083325301, [1] ABDI, H. Partial least squares (pls) regression. Encyclopedia of Social Sciences Research Methods, 3(6):1–7, 2003. [2] ALIN, A. Multicollinearity. Wiley Interdisciplinary Reviews: Computational Statistics, 2010. [3] ARAÚJO, M. C. U.; SALDANHA, T. C. B.; GALVÃO, R. K. H.; YONEYAMA, T.; CHAME, H. C.; VISANI, V. The successive projections algorithm for variable selection in spectroscopicmulticomponent analysis. Chemometrics and Intelligent Laboratory Systems, 57(2):65 – 73, 2001. [4] BEEBE, K.; PELL, R.; SEASHOLTZ, M. Chemometrics: a practical guide. Wiley- Interscience series on laboratory automation. Wiley, 1998. [5] BEKELE, E. G.; NICKLOW, J. W. Multi-objective automatic calibration of SWAT using NSGA-II. Journal of Hydrology, 341(3-4):165–176, Aug. 2007. [6] CHONG, IL-GYO.; JUN, C.-H. Performance of some variable selection methods when multicollinearity is present. Chemometrics and Intelligent Laboratory Systems, v. 78, n. 1-2, (pp. 103-112), 2005. [7] DAVIS, L. Handbook of genetic algorithms. VNR computer library. Van Nostrand Reinhold, 1991. [8] DAWKINS, R. A Escalada do monte improvável: uma defesa da teoria da evolução. Companhia das Letras, 1996. [9] DEB, K.; PRATAP, A. A.-S. . M. T. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. 6, NO. 2, 2002. [10] DEB, K.; KALYANMOY, D. Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, 1 edition, June 2001. [11] DRAPER, R. N. & SMITH, H. Applied regression analysis. Willey series and probability and statistics, 1998. [12] EFSTRATIADIS, A. & KOUTSOYIANNIS, D. One decade of multi-objective calibration approaches in hydrological modelling: a review. Hydrological Sciences Journal, 55(1):58–78, 2010. [13] FERREIRA, M. A. M. C.; ANTUNES, A. M.; MELGO, M. S.; VOLPE, P. L. O. Quimiometria I: calibração multivariada, um tutorial. Química Nova, 22:724 – 731, 09 1999. [14] GALVÃO, R. K. H. A variable elimination method to improve the parsimony of MLR models using the successive projections algorithm. Chemometrics and Intelligent Laboratory Systems, Volume 92, Issue 1, (pp 83-91), 2008. [15] GELADI, P.; KOWALSKI, B. R. Partial least-squares regression: a tutorial. Analytica Chimica Acta, 185(0):1 – 17, 1986. [16] GOLDBERG, D. E. Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, New York, 1989. [17] GUYON, I. An Introduction to Variable and Feature Selection. Journal of Machine Learning Research, [S.l.], v.3, (pp.1157-1182), 2003. [18] HAALAND, D. M. & THOMAS, E. V. Partial Least-Squares Methods for Spectral Analysis 1. Relation to Other Quantitative Calibration Methods and the Extraction of Quantitative Information, Anal. Chem, 60, (pp 1193), 1988. [19] HAWKINS, D. M. The Problem of Overfitting. Journal of Chemical Information and Computer Sciences, 44(1):1–12, 2004. [20] HOCKING, R. R. The analysis and selection of variables in linear regression. Biometrics, 32, pp. 1-49, 1976. [21] HOLLAND, J. H. Adaptation in Natural and Artificial Systems. The University of Michigan Press, 1975. [22] JOHNSON, R. A. & WICHERN, D. W. Applied Multivariate Statistical Analysis. 5th ed. Prentice Hall, New Jersey, 1976. [23] JOLLIFFE, I. T. Principal Component Analysis. Springer, second edition, Oct. 2002. [24] JOLLIFFE, I. T. A Note on the Use of Principal Components in Regression. Applied Statistics, 31(3):300+, 1982. [25] KENNARD, R. . S. L. A. Computer aided design of experiments. Technometrics, 11, (pp 137-148), 1969. [26] KOBAYASHI, M.; ITO, Y.; SAKAUCHI, N.; ODA, I.; KONISHI, I.; TSUNAZAWA, Y. Analysis of nonlinear relation for skin hemoglobin imaging. Opt. Express, 9(13):802–812, Dec 2001. [27] MARTENS, H. & NAES, T. Multivariate Calibration. John Willey & Sons, Chichester, 1989. [28] MICHALEWICZ, Z. Genetic algorithms + data structures = evolution programs. 3 ed. Springer, Berlin, 1996. [29] MILLER, A. Selection of Subsets of Regression Variable. Journal of the Royal Statistical Society. Series A (General), Vol. 147, No. 3, (pp.389-425), 1984. [30] NAES, T.; ISAKSSON, T.; FEARN, T.; DAVIES, T. A User Friendly Guide to Multivariate Calibration and Classification. NIR Publications, Chichester, UK, 2002. [31] NÆS, T.; MEVIK, B.-H. Understanding the collinearity problem in regression and discriminant analysis. Journal of Chemometrics, 15(4):413–426, 2001. [32] NUNES, P. G. A. Uma nova técnica para seleção de variáveis em calibração multivariada aplicada às espectrometrias UV-VIS e NIR, Tese de Doutorado. UFPB/CCEN, João Pessoa, 2008. [33] PIMENTEL, M. F.; BARROS NETO, B. Calibração: Uma revisão para Químicos Analíticos. Química Nova. 19: 268, 1999. [34] RAMSEY, P. H.; HODGES, J. L.; SHAFFER, J. P. Significance probabilities of the wilcoxon signed-rank test. Journal of Nonparametric Statistics, 2(2):133–153, 1993. [35] RENCHER, A. C. Methods of Multivariate Analysis. Willey-Interscience, 2002. [36] SADLER, T.; LANGMAN, J. Langman. Embriología médica: con orientación clínica. Editorial Medica Panamericana Sa de, 2007. [37] SCHULZ, A. G. Fuzzy rule-based expert systems and genetic machine learning. Physica-Verlag, 1997. [38] SKOOG, D. A.; HOLLER, J. F.; CROUCH, S. R. Principles of Instrumental Analysis. Brooks Cole, 6 edition, Dec. 2006. [39] SKOOG, D. A.; LEARY, J. J. Principles of instrumental analysis. 6. ed. New York : Saunders College Publishing, 1992. [40] SNEDECOR, G. W. & COCHRAN, W. G. Statistical Methods. 6 ed., Iowa: Ames, 1972. [41] SOARES(A), A. S.; GALVÃO FILHO, A. R. G. A. R. K. H. . A. M. C. U. Improving the computational efficiency of the successive projections algorithm by using a sequential regression implementation: a case study involving nir spectrometric analysis of wheat samples. J. Braz. Chem. Soc., v. 21, n. 4, São Paulo, 2010. [42] SOARES(B), A. S.; GALVÃO FILHO, A. R. G. A. R. K. H. . A. M. C. U. Multicore computation in chemometrics: case studies of voltammetric and NIR spectrometric analyses. J. Braz. Chem. Soc., v. 21, n. 9, São Paulo, 2010. [43] SRINIVAS, N. & DEB, K. Multiobjective Optimization Using Nondominated Sorting Genetic Algorithms. Evolutionary Computation, Vol.2, No.3, 1994. [44] TAKAHASHI, R. H. C. Otimização Escalar e Vetorial. Universidade Federal de Minas Gerais,[S.l.], 2004. [45] TOBIAS, R. An Introduction to Partial Least Squares Regression. TS-509. SAS Institute Inc., Cary, NC., 1997. [46] WOLD, S.; SJÖSTRÖMA, M. . E. L. PLS-regression: a basic tool of chemometrics. Chemometrics and intelligent laboratory, v. 58, (pp. 109-130), 2001. [47] ZITZLER, E.; LAUMANNS, M.; THIELE, L. Spea2: Improving the strength pareto evolutionary algorithm. Technical report, 2001. [48] ZITZLER, E.; THIELE, L. An evolutionary algorithm for multiobjective optimization: The strength pareto approach, 1998. |
Page generated in 0.0135 seconds