Evolutionary product-unit neural networks classifiers

Neurocomputing

Volumn 72, Issue 1-3, 2008, Pages 548-561

  Martinez Estudillo F J ^a   Hervas Martinez C ^b   Gutierrez P A ^b   Martinez Estudillo A C ^a  

^a UNIVERSIDAD LOYOLA ANDALUCÍA   (Spain)

^b UNIVERSITY OF CÓRDOBA   (Spain)

Author keywords

Classification; Evolutionary neural networks; Product unit neural networks

Indexed keywords

EVOLUTIONARY ALGORITHMS; FEEDFORWARD NEURAL NETWORKS; KETONES; PROBABILITY DENSITY FUNCTION; STATISTICAL TESTS; TREES (MATHEMATICS); VEGETATION;

BASIC STRUCTURES; BENCHMARK DATUMS; CLASSIFICATION; CLASSIFICATION ACCURACIES; CLASSIFICATION METHODS; CROSS ENTROPIES; DECISION RULES; ERROR FUNCTIONS; EVOLUTIONARY COMPUTATIONS; EVOLUTIONARY NEURAL NETWORKS; GROWTH PROBLEMS; LISTERIA; MODEL COEFFICIENTS; MULTINOMIAL LOGISTIC REGRESSIONS; MULTIPLE COMPARISONS; NEURAL NETWORKS CLASSIFIERS; NONLINEAR BASIS FUNCTIONS; PROBABILISTIC INTERPRETATIONS; PRODUCT-UNIT NEURAL NETWORKS; SPECIAL CLASSES; STRONG INTERACTIONS;

NEURAL NETWORKS;

ACCURACY; ANALYTICAL ERROR; ARTICLE; ARTIFICIAL NEURAL NETWORK; BACTERIAL GROWTH; CLASSIFICATION ALGORITHM; CLASSIFIER; DIABETES MELLITUS; ENTROPY; HUMAN; HYPOTHYROIDISM; INTERMETHOD COMPARISON; LISTERIA; MATHEMATICAL MODEL; NONHUMAN; NONLINEAR SYSTEM; PRIORITY JOURNAL; PROBABILITY; QUALITY CONTROL;

EID: 55349128771     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2007.11.019     Document Type: Article

References (64)

1. Abbass H.A. Speeding up backpropagation using multiobjective evolutionary algorithms. Neural Comput. 15 11 (2003) 2705-2726
2. Ahamad N., and Marth E.H. Behaviour of Listeria monocytogenes at 7, 13, 21 and 35 °C in tryptose broth acidified with acetic citric or lactic acid. J. Food Prot. 52 (1989) 688-695
3. J. Alcala-Fdez, L. Sánchez, S. García, M.J.D. Jesus, S. Ventura, J.M. Garrell, J. Otero, C. Romero, J. Bacardit, V.M. Rivas, J.C. Fernández, F. Herrera, KEEL: a software tool to assess evolutionary algorithms for data mining problems, Soft Comput. 2007, accepted for publication.
4. Angeline P.J., Saunders G.M., and Pollack J.B. An evolutionary algorithm that constructs recurrent neural networks. IEEE Trans. Neural Networks 5 1 (1994) 54-65
5. Azevedo M., Padua A.D., and Rodrigues B. Improving generalization of MLPs with sliding mode control and the Levenberg-Marquardt algorithm. Neurocomputing 70 7-9 (2007) 1342-1347
6. Bishop M. Neural Networks for Pattern Recognition (1995), Oxford University Press, Oxford
7. C. Blake, C.J. Merz, UCI repository of machine learning data bases, 1998 〈http://www.ics.uci.edu/mlearn/MLRepository.thmlwww.ics.uci.edu/mlearn/MLRepository.thml〉.
8. Bose S. Classification using splines. Comput. Stat. Data Anal. 22 (1996) 505-525
9. Bose S. Multilayer statistical classifiers. Comput. Stat. Data Anal. 42 (2003) 685-701
10. Conover W.J. Practical Nonparametric Statistics (1971), Wiley, New York
11. A. Chandra, X. Yao. DIVACE: diverse and accurate ensemble learning algorithm, in: Proceedings of the Fifth International Conference on Intelligent Data Engineering and Automated Learning, Lecture Notes in Computer Science, vol. 3177, Springer, Berlin, 2004.
12. A. Chandra, X. Yao, Evolutionary framework for the construction of diverse hybrid ensembles, in: Proceedings of the 13th European Symposium on Artificial Neural Networks, d-side, Brugge, Belgium, 2005.
13. Chandra A., and Yao X. Ensemble learning using multi-objective evolutionary algorithms. J. Math. Modelling Algorithms 5 4 (2006) 417-445
14. Chandra A., and Yao X. Evolving hybrid ensembles of learning machines for better generalization. Neurocomputing 69 7-9 (2006) 686-700
15. Detrano R., Janosi A., Steinbrunn W., Pfisterer M., Schmid J., Sandhu S., Guppy K., Lee S., and Froelicher V. International application of a new probability algorithm for the diagnosis of coronary artery disease. Am. J. Cardiol. 64 (1989) 304-310
16. Durbin R., and Rumelhart D. Products units: a computationally powerful and biologically plausible extension to backpropagation networks. Neural Comput. 1 (1989) 133-142
17. Engelbrecht A.P., and Ismail A. Training product unit neural networks. Stability Control: Theory Appl. 2 1-2 (1999) 59-74
18. European Commission, Opinion of the scientific committee on veterinary measures relating to public health on Listeria monocytogenes, 1999 〈http://www.europa.eu.int/comm/food/fs/sc/scv/out25〉.
19. Fogel D.B. Using evolutionary programming to greater neural networks that are capable of playing Tic-Tac-Toe. International Conference on Neural Networks (1993), IEEE Press, San Francisco, CA
20. Fogel D.B. Evolutionary Computation: Toward a New Philosophy of Machine Intelligence (1995), IEEE Press, New York
21. Fogel D.B., Owens A.J., and Wals M.J. Artificial Intelligence Through Simulated Evolution (1966), Wiley, New York
22. Friedman J. Multivariate adaptive regression splines (with discussion). Ann. Stat. 19 (1991) 1-141
23. Friedman J., and Stuetzle W. Proyection pursuit regression. J. Am. Stat. Assoc. 76 376 (1981) 817-823
24. García-Pedrajas N., Hervás-Martínez C., and Muñoz-Pérez J. Multiobjective cooperative coevolution of artificial neural networks. Neural Networks 15 10 (2002) 1255-1274
25. García-Pedrajas N., Hervás-Martínez C., and Muñoz-Pérez J. COVNET: a cooperative coevolutionary model for evolving artificial neural networks. IEEE Trans. Neural Networks 14 3 (2003) 575-596
26. George S.M., Lund B.M., and Brocklehurst T.F. The effect of pH and temperature on the initiation of growth of Listeria monocytogenes. Lett. Appl. Microbiol. 6 (1988) 153-156
27. Gepperth A., and Roth S. Applications of multi-objective structure optimization. Neurocomputing 69 7-9 (2006) 701-713
28. Gysemans K.P.M., Bernaerts K., Vermeulen A., Geeraerd A.H., Debevere J., Devlieghere F., and Impe J.F.V. Exploring the performance of logistic regression model types on growth/no growth data of Listeria monocytogenes. Int. J. Food Microbiol. 114 3 (2007) 316-331
29. Hajmeer M.N., and Basheer I.A. Comparison of logistic regression and neural network-based classifier for bacterial growth. Food Microbiol. 20 (2003) 43-55
30. Hastie T., Tibshirani R.J., and Friedman J. The elements of statistical learning. Data Mining, Inference and Prediction (2001), Springer, Berlin
31. Hastie T.J., and Tibshirani R.J. Generalized Additive Models (1990), Chapman & Hall, London
32. Hervás C., and Martínez-Estudillo F.J. Logistic regression using covariates obtained by product-unit neural network models. Pattern Recognition 40 (2007) 52-64
33. Huang G.-B., and Chen L. Convex incremental extreme learning machine. Neurocomputing 70 16-18 (2007) 3056-3062
34. Huang G.-B., Chen L., and Siew C.-K. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans. Neural Networks 17 4 (2006) 879-892
35. Huang G.-B., Zhu Q.-Y., and Siew C.-K. Extreme learning machine: theory and applications. Neurocomputing 70 (2006) 489-501
36. G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, Extreme learning machine: a new learning scheme of feedforward neural networks, in: Proceedings of the International Joint Conference on Neural Networks (IJCNN2004), Budapest, Hungary, 2004.
37. Hwang C.-A., and Tamplin M.L. The influence of mayonnaise pH and storage temperature on the growth of Listeria monocytogenes in seafood salad. Int. J. Food Microbiol. 102 (2005) 277-285
38. A. Ismail, A.P. Engelbrecht, Training products units in feedforward neural networks using particle swarm optimization, in: V.B. Bajic, D. Sha (Eds.), Development and Practice of Artificial Intelligence Techniques, Proceeding of the International Conference on Artificial Intelligence, Durban, South Africa, 1999.
39. A. Ismail, A.P. Engelbrecht, Global optimization algorithms for training product units neural networks, in: International Joint Conference on Neural Networks IJCNN'2000, Como, Italy, 2000.
40. A. Ismail, A.P. Engelbrecht, Pruning product unit neural networks, in: Proceedings of the International Conference on Neural Networks, Honolulu, Hawaii, 2002.
41. Janson D.J., and Frenzel J.F. Training product unit neural networks with genetic algorithms. IEEE Expert 8 5 (1993) 26-33
42. Kirkpatric S., Gellat C.D.J., and Vecchi M.P. Optimization by simulated annealing. Science 220 (1983) 671-680
43. Kooperberg C., Bose S., and Stone C.J. Polychotomous regression. J. Am. Stat. Assoc. 92 (1997) 117-127
44. Leerink L.R., Giles C.L., Horne B.G., and Jabri M.A. Learning with products units. Adv. Neural Networks Process. Syst. 7 (1995) 537-544
45. H. Levene, Essays in honor of Harold Hotelling, in: Contributions to Probability and Statistics, 1960, pp. 278-292.
46. Martinez-Estudillo A.C., Martinez-Estudillo F.J., Hervas-Martinez C., and Garcia-Pedrajas N. Evolutionary product unit based neural networks for regression. Neural Networks 19 4 (2006) 477-486
47. Martinez-Estudillo A.C., Hervas-Martinez C., Martinez-Estudillo F.J., and Garcia-Pedrajas N. Hybridization of evolutionary algorithms and local search by means of a clustering method. IEEE Trans. Syst. Man Cybern. 36 3 (2006) 534-545
48. Miller G.F., Todd P.M., and Hedge S.U. Designing neural networks using genetic algorithms. Proceedings of the Third International Conference Genetic Algorithms and their Applications (1989), Morgan Kaufmann, San Mateo, CA
49. Otten R.H.J.M., and van Ginneken L.P.P.P. The Annealing Algorithm (1989), Kluwer, Boston, MA
50. Potter M.A., and de Jong K.A. Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol. Comput. 8 1 (2000) 1-29
51. Reed R. Pruning algorithms-a survey. IEEE Trans. Neural Networks 4 (1993) 740-747
52. Rocha M., Cortez P., and Neves J. Evolution of neural networks for classification and regression. Neurocomputing 70 (2007) 2809-2816
53. K. Saito, R. Nakano, Numeric law discovery using neural networks, in: Proceedings of the Fourth International Conference on Neural Information Processing (ICONIP97), 1997.
54. Saito K., and Nakano R. Extracting regression rules from neural networks. Neural Networks 15 (2002) 1279-1288
55. Schmitt M. On the complexity of computing and learning with multiplicative neural networks. Neural Comput. 14 (2001) 241-301
56. Setiono R., and Hui L.C.K. Use of quasi-Newton method in a feedforward neural-network construction algorithm. IEEE Trans. Neural Networks 6 (1995) 273-277
57. SPSS I. SPSS© para Windows 11.0.1 (1989-2001), SPSS Inc., Chicago, IL
58. Vapnik V.N. The Nature of Statistical Learning Theory (1999), Springer, Berlin
59. Ventura S., Romero C., Zafra A., Delgado J.A., and Hervás-Martínez C. JCLEC: a JAVA framework for evolutionary computation. Soft Computing-A Fusion of Foundations, Methodologies and Applications 12 4 (2007) 381-392
60. Yan W., Zhu Z., and Hu R. Hybrid genetic /BP algorithm and its application for radar target classification. Proceedings of the IEEE National Aerospace Electronics Conference (1997), IEEE Press, Piscataway, NJ
61. Yao X. Evolving artificial neural network. Proc. IEEE 9 87 (1999) 1423-1447
62. Yao X., and Liu Y. A new evolutionary system for evolving artificial neural networks. IEEE Trans. Neural Networks 8 3 (1997) 694-713
63. Yao X., and Liu Y. Making use of population information in evolutionary artificial neural networks. IEEE Trans. Syst. Man Cybern. 28 3 (1998) 417-425
64. Zhu Q.-Y., Qin A.K., Suganthan P.N., and Huang G.-B. Evolutionary extreme learning machine. Pattern Recognition 38 (2005) 1759-1763