Multinomial logistic regression and product unit neural network models: Application of a new hybrid methodology for solving a classification problem in the livestock sector

Expert Systems with Applications

Volumn 36, Issue 10, 2009, Pages 12225-12235

  Torres, Mercedes ^a   Hervás, Cesar ^b   García, Carlos ^a  

^a UNIVERSIDAD LOYOLA ANDALUCÍA   (Spain)

^b UNIVERSITY OF CÓRDOBA   (Spain)

Author keywords

Classification; Evolutionary computation; Livestock application; Neural networks; Product units

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BUSINESS MANAGEMENT; CLASSIFICATION; COVARIATES; EVOLUTIONARY COMPUTATION; HYBRID METHODOLOGIES; LIVESTOCK APPLICATION; MILK PRODUCTION; MULTICLASS PATTERN RECOGNITION; MULTINOMIAL LOGISTIC REGRESSION; NEW APPROACHES; NON-LINEAR MODEL; NONLINEAR BASIS FUNCTIONS; PRODUCT UNIT NEURAL NETWORK; PRODUCT UNITS; PRODUCT-UNIT; PRODUCTIVE CAPACITY; SELECTION PROCESS; TECHNOLOGICAL INNOVATION;

EVOLUTIONARY ALGORITHMS; LOGISTICS; NEURAL NETWORKS; PATTERN RECOGNITION; REGRESSION ANALYSIS; TECHNOLOGICAL FORECASTING;

AGRICULTURE;

EID: 69249217895     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.04.070     Document Type: Article

References (61)

1. Bernadó E., and Garrell J.M. Accuracy-based learning classifier systems: Models, analysis and applications to classification tasks. Evolutionary Computation 11 3 (2003) 209-238
2. Bebis M., and Georgipoulos M. Coupling weight elimination with genetic algorithms to reduce network size and preserve generalization. Neurocomputing 17 (1997) 167-194
3. Bishop M. Neural networks for pattern recognition (1995), Oxford University Press, New York
4. Breiman L. Technical note: Some properties of splitting criteria. Machine Learning 24 1 (1996) 41-47
5. Buxadé C. Ovino de leche: aspectos clave. (1998), Mundi Prensa, Madrid
6. Cox D.R. Analysis of binary data (1970), Methuen & G, London
7. Cox D.R., and Snell E.J. Analysis of binary data. 2nd ed. (1989), Chapman & Hall, London
8. Dasarathy B. Nearest neighbour pattern classification techniques (1991), IEEE Computer Society Press, Silver Spring, MD
9. Doumpos M., Zopounidis C., and Pardalos P. Multicriteria sorting methodology: Application to financial decision problems. Parallel Algorithms and Applications 15 1-2 (2000) 113-129
10. Dreiseitl S., and Ohno-Machado L. Logistic regression and artificial neural network classification models: A methodology review. Journal of Biomedical Informatics 35 (2002) 352-359
11. Duda R., and Hart P. Pattern classification (2001), John Wiley
12. Durbin R., and Rumelhart D. Product units: A computationally powerful and biologically plausible extension to backpropagation networks. Neural Computation 1 (1989) 133-142
13. Engelbrecht, A. P., & Ismail, A. (2000). Global optimization algorithms for training product units neural networks. In Presented at international joint conference on neural networks IJCNN'2000, Italy.
14. Fredd N., and Glover F. Simple but powerful goal programming models for discrimination problems. European Journal of Operational Research 7 (1981) 44-60
15. Friedman J., and Stuetzle W. Projection pursuit regression. American Statistical Association 76 (1981) 817-823
16. Friedman J. Multivariate adaptive regression splines. Annals of Statistics 19 1 (1991) 1-67
17. Gallego L., and Bernabeu R. Producción de leche: factores de variación en ganado ovino de raza Manchega. (pp. 162-173) (1994), Mundi Prensa, Madrid
18. Goldberg D.E. Genetic algorithms in search optimization and machine learning (1989), Addison-Wesley
19. Goldberg D.E. Genetic algorithms and Walsh functions. Part 2: Deception and its analysis. Complex Systems 3 (1989) 153-171
20. Goldberg D.E. Genetic algorithms and Walsh functions. Part 1: A gentle introduction. Complex Systems 3 (1989) 129-152
21. Hajmeer M., and Basheer I. Comparison of logistic regression and neural network-based classifiers for bacterial growth. Food Microbiology 20 (2003) 43-55
22. Hastie T.J., and Tibshirani R.J. Generalized additive models (1990), Chapman & Hall, London
23. Hayken S. A comprehensive foundation. Neural networks (1994), Macmillan, Nueva York
24. Hervás C., and Martínez F. Logistic regression using covariates obtained by product-unit neural network models. Pattern Recognition 40 1 (2007) 52-64
25. Honaver, V., & Balakrishnan, K. (1998). Evolutionary design of neural architectures. A preliminary taxonomy and guide to literature.
26. Hosmer D., and Lemeshow S. Applied logistic regression. 2nd ed. (2000), Wiley, New York
27. Ismail, A., & Engelbrecht, A. P. (1999). Training product units in feedforward neural networks using particle swarm optimisation. Durban, South Africa.
28. Johnson R.A., and Wichern D.W. Applied multivariate statistical analysis. 5th ed. (2002), Prentice Hall, Upper Saddle Rover, NJ
29. Kaefer F., Heilman C.M., and Ramenofsky S.D. A neural network application to consumer classification to improve the timing of direct marketing activities. Computers and Operations Research 32 10 (2005) 2595-2615
30. Kooperberg C., Bose S., and Stone C.J. Polychotomous regression. Journal of the American Statistical Association 92 (1997) 117-127
31. Kordatoff, Y., & Michlaski, R. S. 1990. Machine learning: An artificial intelligence approach (Vol. II).
32. Lin C., Lee J.-H., and Lee C.-J. A novel hybrid learning algorithm for parametric fuzzy CMAC networks and its classification applications. Expert Systems with Applications 35 4 (2008) 1711-1720
33. Major R., and Ragsdale C. Aggregating expert predictions in a networked environment. Computers and Operations Research 28 (2001) 1231-1244
34. Martínez A.C., Hervás C., Martínez F.J., et al. Hybridizationof evolutionary algorithms and local search by means of a clustering method. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics 36 3 (2006) 534-546
35. Martínez A.C., Martínez F.J., Hervás C., et al. Evolutionary product unit based neural networks for regression. Neural Networks 19 (2006) 477-486
36. McCullagh P., and Nelder J.A. Generalized linear models. 2nd ed. (1989), Chapman and Hall, Norwell, MA
37. Molina, M. P. (1987). Composición y factores de variación de la leche de ovejas de raza Manchega. Tesis doctoral Universidad de Valencia, España.
38. Montoro, V., & Pérez-Guzmán, M. D. (1996). La selección de la raza ovina Manchega. Junta de Comunidades de Castilla la Mancha, No 9.
39. Minka T. A comparison of numerical optimizers for logistic regression (2003), Department of Statistics, Carnegie Mellon University
40. Orr, M. J. L. (1996). Introduction to radial basis function networks. Technical report. Center for Cognitive Science, University of Edinburgh, UK.
41. Oyang Y.-J., Hwang S.-C., Ou Y.-Y., Chen C.-Y., and Chen Z.-W. Data classification with radial basis function networks based on a novel kernel density estimation algorithm. Transactions on Neural Networks January (2005)
42. Parag C., and Pendharkar A. A threshold-varying artificial neural network approach for classification and its application to bankruptcy prediction problem. Computers and Operations Research 32 10 (2005) 2561-2582
43. Patuwo E., Hu M., and Hung M.S. Two group classification using neural networks. Decision Sciences 24 (1993) 825-845
44. Pérez S., and Gracia O. Factores ambientales que influyen en la producción lechera en la raza ovina manchega (1994), Universidad de Castilla-La Mancha, España
45. Purroy, A. (1982). Producción de leche de oveja. Monografías I.N.I.A., 36, Madrid.
46. Quinlan J.R. C4.5: programs for machine learning (1993), Morgan Kaufmann, San Mateo, CA
47. Quinlan J.R. Induction of decision trees. Machine Learning 1 (1986) 81-106
48. Rada R. Expert system and evolutionary computing for financial investing: A review. Expert Systems with Applications 34 (2008) 2232-2240
49. Rechenberg I. Evolutionstrategie: Optimierung technischer Systeme nach Prinzipien der Biologischen Evolution (1975), Framman-Holzboog Verlag, Stuttgart
50. Schwarzer G., Vach W., and Schumacher M. On the misuses of artificial neural networks for prognostic and diagnostic classification in oncology. Statistics in Medicine 19 (2000) 541-561
51. Serrano M., and Montoso V. Cálculo de los factores de extensión de la lactación a 120 días en ganado ovino Manchego. Investigación agraria: Producción y Sanidad Animales 11 1 (1996) 69-83
52. Sexton R., and McMurtrey S. Employee turnover: A neural network solution. Computers and Operation Research 32 10 (2005) 2635-2651
53. Schmitt M. On the complexity of computing and learning with multiplicative neural networks. Neural Computation 14 (2002) 241-301
54. Subasi A., Alkan A., and Koklukaya E. Wavelt neural network classification of EEG signals by using AR model with MLE preprocessing. Neural Networks 18 (2005) 985-997
55. Tian-Shyug L., Chih-Chou C., et al. Mining the customer credit using classification and regression tree and multivariate adaptive regression splines. Computational Statistics and Data Analysis 50 (2006) 1113-1130
56. Torres M., Hervás C., and Amador F. Approximating the sheep milk production curve through the use of artificial neural networks and genetic algorithms. Computers and Operation Research 32 (2005) 2653-2670
57. Widrow B. Layered neural nets for pattern recognition. IEEE 36 (1962) 1109-1118
58. Williams, R. J., & Minai, A. A. (1990). Back-propagation heuristics: A study of the extended delta-bar-delta algorithms. In IEEE international joint conference on neural networks (Vol. 1, pp. 595-600).
59. Witten, I. H., & Frank, E. (2000). Weka machine learning. Release 3.4.0, 2000.
60. Youngdai K., and Sunghoon K. Multiclass sparse logistic regression for classification of multiple cancer types using gene expression data. Computational Statistics and Data Analysis 51 3 (2006) 1643-1655
61. Yuan-chin, I., & Sung-Chiang, L. (2004). Synergy of logistic regression and support vector machine in multiple-class classification. IDEAL. LNCS (Vol. 3177, pp. 132-141).