Memetic Pareto differential evolutionary neural network used to solve an unbalanced liver transplantation problem

Soft Computing

Volumn 17, Issue 2, 2013, Pages 275-284

  Cruz Ramirez M ^a   Hervas Martinez C ^a   Gutierrez P A ^a   Perez Ortiz M ^a   Briceno J ^b   de la Mata, M ^b  

^a UNIVERSITY OF CÓRDOBA   (Spain)

^b HOSPITAL UNIVERSITARIO REINA SOFÍA   (Spain)

Author keywords

Artificial neural networks; Liver transplantation; Multi objective evolutionary algorithm; Organ allocations; Pre processing

Indexed keywords

COMPLEX NETWORKS; DECISION MAKING; EVOLUTIONARY ALGORITHMS; PROCESSING;

ARTIFICIAL NEURAL NETWORK MODELS; DECISION MAKING PROCESS; DIFFERENTIAL EVOLUTIONARY; LIVER TRANSPLANTATION; MULTI OBJECTIVE EVOLUTIONARY ALGORITHMS; ORGAN ALLOCATIONS; PRE-PROCESSING; PRE-PROCESSING METHOD;

NEURAL NETWORKS;

EID: 84872820048     PISSN: 14327643     EISSN: 14337479     Source Type: Journal    
DOI: 10.1007/s00500-012-0892-7     Document Type: Article

References (35)

1. Abbass HA, Sarker R, Newton C (2001) PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems. In: Proceedings of the 2001 Congress on Evolutionary Computation, Seoul, South Korea, vol 2.
2. Ahandani M, Shirjoposh N, Banimahd R (2011) Three modified versions of differential evolution algorithm for continuous optimization. Soft Comput 15(4): 803-830.
3. Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford.
4. Bishop CM (2006) Pattern recognition and machine learning. Springer, Berlin.
5. Caruana R, Niculescu-Mizil A (2004) Data mining in metric space: an empirical analysis of supervised learning performance criteria. In: KDD-2004-Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, pp 69-78.
6. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2: 27: 1-27: 27.
7. Chawla N, Bowyer K, Hall L, Kegelmeyer W (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16: 321-357.
8. Coello Coello C, Lamont G, Veldhuizen D (2007) Evolutionary algorithms for solving multi-objective problems, 2nd edn. Springer, Berlin.
9. Cruz-Ramírez M, Sánchez-Monedero J, Fernández-Navarro F, Fernández J, Hervás-Martínez C (2010) Memetic pareto differential evolutionary artificial neural networks to determine growth multi-classes in predictive microbiology. Evol Intell 3(3-4): 187-199.
10. Cruz-Ramírez M, Fernández J, Fernández-Navarro F, Briceño J, de la Mata M, Hervás-Martínez C (2011) Memetic evolutionary multi-objective neural network classifier to predict graft survival in liver transplant patients. In: Genetic and evolutionary computation conference (GECCO2011), pp 479-486.
11. Dvorchik I, Subotin M, Marsh W, McMichael J, Fung J (1996) Performance of multi-layer feedforward neural networks to predict liver transplantation outcome. Methods Inf Med 35: 12-18.
12. Farias G, Santos M, López V (2010) Making decisions on brain tumor diagnosis by soft computing techniques. Soft Comput 14(12): 1287-1296.
13. Fernández JC, Hervás C, Martínez FJ, Gutiérrez PA, Cruz M (2009) Memetic Pareto differential evolution for designing artificial neural networks in multiclassification problems using cross-entropy versus sensitivity. In: Hybrid artificial intelligence systems, vol 5572. Springer, Berlin, pp 433-441.
14. Fernández JC, Martínez-Estudillo FJ, Hervás-Martínez C, Gutiérrez PA (2010) Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks. IEEE Trans Neural Netw 21(5): 750-770.
15. Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugen 7(7): 179-188.
16. Furness P, Levesley J, Luo Z, Taub N, Kazi J, Bates W, Nicholson M (1999) A neural network approach to the biopsy diagnosis of early acute renal transplant rejection. Histopathology 35(5): 461-467.
17. Gutiérrez PA, Hervás C, Lozano M (2010) Designing multilayer perceptrons using a guided saw-tooth evolutionary programming algorithm. Soft Comput 14(6): 599-613.
18. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. ACM SIGKDD Explor Newsl 11: 10-18.
19. Haykin S (1998) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall, Upper Saddle River.
20. Hodge VJ, Austin J (2004) A survey of outlier detection methodologies. Artif Intell Rev 22: 2004.
21. Igel C, Hüsken M (2003) Empirical evaluation of the improved rprop learning algorithms. Neurocomputing 50(6): 105-123.
22. Jarman I, Etchells T, Bacciu D, Garibaldi J, Ellis I, Lisboa P (2011) Clustering of protein expression data: a benchmark of statistical and neural approaches. Soft Comput 15(8): 1459-1469.
23. Kondo T (2007) Evolutionary design and behavior analysis of neuromodulatory neural networks for mobile robots control. Appl Soft Comput 7: 189-202.
24. Landwehr N, Hall M, Frank E (2005) Logistic model trees. Mach Learn 59(1-2): 161-205.
25. Löfström T, Johansson U, Boström H (2009) Ensemble member selection using multi-objective optimization. In: IEEE symposium on computational intelligence and data mining, pp 245-251.
26. Matis S, Doyle H, Marino I, Mural R, Uberbacher E (1995) Use of neural networks for prediction of graft failure following liver transplantation. IEEE symposium on computer-based medical systems, pp 133-140.
27. Ramasubramanian P, Kannan A (2006) A genetic-algorithm based neural network short-term forecasting framework for database intrusion prediction system. Soft Comput 10(8): 699-714.
28. Richard D, David ER (1989) Product units: a computationally powerful and biologically plausible extension to backpropagation networks. Neural Comput 1(1): 133-142.
29. Rivero D, Dorado J, Rabuñal J, Pazos A (2009) Modifying genetic programming for artificial neural network development for data mining. Soft Comput 13(3): 291-305.
30. Saxena A, Saad A (2007) Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems. Appl Soft Comput 7: 441-454.
31. Sheppard D, McPhee D, Darke C, Shrethra B, Moore R, Jurewitz A, Gray A (1999) Predicting cytomegalovirus disease after renal transplantation: an artificial neural network approach. Int J Med Inf 54(1): 55-76.
32. Storn R, Price K (1997) Differential evolution. A fast and efficient heuristic for global optimization over continuous spaces. J Global Optim 11: 341-359.
33. Theodoridis S, Koutroumbas K (2006) Pattern Recognit. Academic Press, Elsevier.
34. Wiesner R, Edwards E, Freeman R, Harper A, Kim R, Kamath P, Kremers W, Lake J, Howard T, Merion R, Wolfe R, Krom R, Colombani P, Cottingham P, Dunn S, Fung J, Hanto D, McDiarmid S, Rabkin J, Teperman L, Turcotte J, Wegman L (2003) Model for end-stage liver disease (MELD) and allocation of donor livers. Gastroenterology 124(1): 91-96.
35. Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques. In: Data management systems, 2nd edn. Morgan Kaufmann (Elsevier), New York.