A two-stage evolutionary algorithm based on sensitivity and accuracy for multi-class problems

Information Sciences

Volumn 197, Issue , 2012, Pages 20-37

  Gutiérrez, Pedro Antonio ^a   Hervás Martínez, César ^a   Martínez Estudillo, Francisco José ^b   Carbonero, Mariano ^b  

^a UNIVERSITY OF CÓRDOBA   (Spain)

^b UNIVERSIDAD LOYOLA ANDALUCÍA   (Spain)

Author keywords

Accuracy; Classification; Imbalanced datasets; Multi class; Sensitivity; Two stage evolutionary algorithm

Indexed keywords

ACCURACY; IMBALANCED DATASETS; MULTI-CLASS; SENSITIVITY; TWO STAGE;

CLASSIFIERS; HEALTH; LEARNING ALGORITHMS; LEARNING SYSTEMS;

CLASSIFICATION (OF INFORMATION);

EID: 84859099327     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2012.02.012     Document Type: Article

References (40)

1. H. Abbass An evolutionary artificial neural networks approach for breast cancer diagnosis Artificial Intelligence in Medicine 25 3 2002 265 281
2. J. Alcala-Fdez, L. Sánchez, S. García, M.J. del Jesús, S. Ventura, J.M. Garrell, J. Otero, C. Romero, J. Bacardit, V.M. Rivas, J.C. Fernández, and F. Herrera KEEL: a software tool to assess evolutionary algorithms for data mining problems Soft Computing 13 3 2008 307 318
3. P.J. Angeline, G.M. Sauders, and J.B. Pollack An evolutionary algorithm that constructs recurrent neural networks IEEE Transactions on Neural Networks 5 1994 54 65
4. A. Asuncion, D. Newman, UCI Machine Learning Repository, 2007. < http://www.ics.uci.edu/learn/MLRepository.html >.
5. C.M. Bishop Pattern Recognition and Machine Learning 1st ed. Information Science and Statistics 2006 Springer
6. P.A. Castillo, J.J. Merelo, M.G. Arenas, and G. Romero Comparing evolutionary hybrid systems for design and optimization of multilayer perceptron structure along training parameters Information Sciences 177 14 2007 2884 2905
7. C.-C. Chang, C.-J. Lin. LIBSVM: A Library for Support Vector Machines, 2001. < http://www.csie.ntu.edu.tw/cjlin/libsvm >.
8. M.-C. Chen, L.-S. Chen, C.-C. Hsu, and W.-R. Zeng An information granulation based data mining approach for classifying imbalanced data Information Sciences 178 16 2008 3214 3227
9. J. Cohen A coefficient of agreement for nominal scales Educational and Psychological Measurement 20 1 1960 37 46
10. E. Commision, 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 >.
11. O.J. Dunn Multiple comparisons among means Journal of the American Statistical Association 56 1961 52 56
12. R. Everson, and J. Fieldsend Multi-class ROC analysis from a multi-objective optimisation perspective Pattern Recognition Letters 27 8 2006 918 927
13. T. Fawcett An introduction to ROC analysis Pattern Recognition Letters 27 2006 861 874
14. J.C. Fernández-Caballero, F.J. Martínez-Estudillo, C. Hervás-Martínez, and P.A. Gutiérrez Sensitivity versus accuracy in multiclass problems using memetic pareto evolutionary neural networks IEEE Transactions on Neural Networks 21 5 2010 750 770
15. D. Fogel Evolutionary Computation: Toward a New Philosophy of Machine Intelligence 1995 IEEE Press New York
16. M. Friedman A comparison of alternative tests of significance for the problem of m rankings Annals of Mathematical Statistics 11 1 1940 86 92
17. P.A. Gutiérrez, C. Hervás-Martínez, and F.J. Martínez-Estudillo Logistic regression by means of evolutionary radial basis function neural networks IEEE Transactions on Neural Networks 22 2 2011 246 263
18. T. Hastie, R. Tibshirani, and J.H. Friedman The Elements of Statistical Learning 2001 Springer
19. C. Hervás-Martínez, F.J. Martínez-Estudillo, and M. Carbonero-Ruz Multilogistic regression by means of evolutionary product-unit neural networks Neural Networks 21 7 2008 951 961
20. C. Hervás-Martínez, M. Silva, P.A. Gutiérrez, and A. Serrano Multilogistic regression by evolutionary neural network as a classification tool to discriminate highly overlapping signals: Qualitative investigation of volatile organic compounds in polluted waters by using headspace-mass spectrometric analysis Chemometrics and Intelligent Laboratory Systems 92 2 2008 179 185
21. Y. Hochberg, and A. Tamhane Multiple Comparison Procedures 1987 John Wiley & Sons
22. C.-W. Hsu, and C.-J. Lin A comparison of methods for multi-class support vector machines IEEE Transaction on Neural Networks 13 2 2002 415G 425
23. J. Huang, and C.X. Ling Using AUC and accuracy in evaluating learning algorithms IEEE Transactions on Knowledge and Data Engineering 17 3 2005 299 310
24. T. Landgrebe, and R.P. Duin Efficient multiclass ROC approximation by decomposition via confusion matrix perturbation analysis IEEE Transactions on Pattern Analysis and Machine Intelligence 30 5 2008 810 822
25. N. Landwehr, M. Hall, and E. Frank Logistic model trees Machine Learning 59 1-2 2005 161 205
26. H. Mamitsuka Selecting features in microarray classification using ROC curves Pattern Recognition 39 12 2006 2393 2404
27. C. Marrocco, R.P.W. Duin, and F. Tortorella Maximizing the area under the ROC curve by pairwise feature combination Pattern Recognition 41 6 2008 1961 1974
28. F.J. Martínez-Estudillo, P.A. Gutiérrez, C. Hervás-Martínez, and J.C. Fernández Evolutionary learning by a sensitivity-accuracy approach for multi-class problems Proceedings of the 2008 IEEE Congress on Evolutionary Computation (CEC'08) 2008 IEEE Press Hong Kong, China 1581 1588
29. F.J. Martínez-Estudillo, C. Hervás-Martínez, P.A. Gutiérrez, and A.C. Martínez-Estudillo Evolutionary product-unit neural networks classifiers Neurocomputing 72 1-2 2008 548 561
30. R. Pasti, and L.N. de Castro Bio-inspired and gradient-based algorithms to train MLPS: the influence of diversity Information Sciences 179 10 2009 1441 1453
31. L. Prechelt, PROBEN1: A Set of Neural Network Benchmark Problems and Benchmarking Rules. Tech. Rep. 21/94, Fakultät für Informatik (Universität Karlsruhe), 1994.
32. F. Provost, and T. Fawcett Analysis and visualization of the classifier performance: comparison under imprecise class and cost distribution Proceedings of the Third International Conference on Knowledge Discovery (KDD97) and Data Mining 1997 AAAI Press 43 88
33. F. Provost, T. Fawcett, Robust classification system for imprecise environments, in: Proceedings of the Fifteenth National Conference on Artificial Intelligence, 1998, p. 706-713.
34. J.R. Quinlan C4.5: Programs for Machine Learning 1993 Morgan Kaufmann
35. A. Valero, C. Hervas, R.M. Garcia-Gimeno, and G. Zurera Product unit neural network models for predicting the growth limits of listeria monocytogenes Food Microbiology 24 5 2007 452 464
36. S. Ventura, C. Romero, A. Zafra, J.A. Delgado, and C. Hervas JCLEC: a Java framework for evolutionary computation Soft Computing 12 4 2008 381 392
37. I.H. Witten, and E. Frank Data Mining: Practical Machine Learning Tools and Techniques 2nd ed. Data Management Systems 2005 Morgan Kaufman (Elsevier)
38. X. Yao Evolving artificial neural networks Proceedings of the IEEE 87 9 1999 1423 1447
39. X. Yao, and Y. Liu A new evolutionary system for evolving artificial neural networks IEEE Transactions on Neural Networks 8 3 1997 694 713
40. M.J. Zolghadri, and E.G. Mansoori Weighting fuzzy classification rules using receiver operating characteristics (ROC) analysis Information Sciences 177 11 2007 2296 2307