Extracting the contribution of independent variables in neural network models: A new approach to handle instability

Neural Computing and Applications

Volumn 25, Issue 3-4, 2014, Pages 859-869

  de Oña, Juan ^a   Garrido, Concepción ^a  

^a UNIVERSITY OF GRANADA   (Spain)

Author keywords

Black box; Importance ranking; Instability; Neural networks; Variables contribution's methods

Indexed keywords

CUSTOMER SATISFACTION; NETWORK ARCHITECTURE; NEURAL NETWORKS; PLASMA STABILITY;

BLACK BOXES; CUSTOMER SATISFACTION SURVEY; EXPLANATORY VARIABLES; IMPORTANCE RANKING; INDEPENDENT VARIABLES; NEURAL NETWORK MODEL; PUBLIC TRANSPORT SYSTEMS; VARIABLES CONTRIBUTION'S METHODS;

DEEP NEURAL NETWORKS;

EID: 84896421866     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-014-1573-5     Document Type: Article

References (55)

1. Akin D, Akbaç B (2010) A neural network (NN) model to predict intersection crashes based upon driver, vehicle and roadway surface characteristics. Sci Res Essays 5(19): 2837-2847.
2. Azadeh A, Rouzbahman M, Saberi M, Fam IM (2011) An adaptative neural network algorithm for assessment and improvement of job satisfaction with respect to HSE and ergonomics program: the case of a gas refinery. J Loss Prev Process Ind 24: 361-370.
3. Beale MH, Hagan MT, Demuth HB (2007) Neural Network Toolbox 7. User's Guide. MathWorks, Inc. 3 Apple Hill Drive Natic, MA 01760-2098.
4. Cao M, Qiao P (2008) Neural network committee-based sensitivity analysis strategy for geotechnical engineering problems. Neural Comput Appl 17: 509-519.
5. Cortez P, Embrechts MJ (2011) Opening black box data mining models using sensitivity analysis. IEEE symposium series in computational intelligence, Paris, France, 4, 2011.
6. Cortez P, Embrechts MJ (2013) Using sensitivity analysis and visualization techniques to open black box data mining models. Inf Sci 225: 1-17.
7. De Oña J, De Oña R, Calvo FJ (2012) A classification tree approach to identify key factors of transit service quality. Expert Syst Appl 39: 11164-11171.
8. De Oña J, De Oña R, Eboli L, Mazzulla G (2013) Perceived service quality in bus transit service: a structural equation approach. Transp Policy 29: 219-226.
9. Delen D, Sharda R, Bessonov M (2006) Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks. Accid Anal Prev 38: 434-444.
10. Dell'Olio L, Ibeas A, Cecín P (2010) Modelling user perception of bus transit quality. Transp Policy 17(6): 388-397.
11. Dell'Olio L, Ibeas A, Cecín P (2011) The quality of service desired by public transport users. Transp Policy 18(1): 217-227.
12. Dimopoulos Y, Bourret P, Lek S (1995) Use of some sensitivity criteria for choosing networks with good generalization ability. Neural Process Lett 2: 1-4.
13. Eboli L, Mazulla G (2008) Willingness-to-pay of public transport users for improvement in service quality. Eur Transp 38: 107-118.
14. Eboli L, Mazulla G (2010) How to capture the passengers' point of view on a transit service through rating and choice opinions. Transp Rev 30: 435-450.
15. Engelbrecht AP, Cloete I, Zurada JM (1995) Determining the significance of input parameters using sensitivity analysis, from natural to artificial neural computation. In: proceedings of International Workshop on Artificial Neural Networks. Málaga-Torremolinos, Springer, Spain, pp 382-388.
16. Funahashi KI (1989) On the approximate realization of continuous mappings by neural networks. Neural Netw 2: 183-192.
17. Garson GD (1991) Interpreting neural-network connection weights. Artif Intell Expert 6: 47-51.
18. Gedeon, T. D., Wong, P. M. & Harris, D., (1995). Balancing the bias and variance: network topology and pattern set reduction techniques. In: proceedings of International Workshop on Artificial Neural Networks, IWANN95, Torremolinos, España, pp 550-558.
19. Gedeon TD (1997) Data mining of inputs: analyzing magnitude of functional measures. Int J Neural Syst 8(2): 209-218.
20. Gevrey M, Dimopoulos I, Lek S (2003) Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecol Model 160: 249-264.
21. Gevrey M, Dimopoulos I, Lek S (2006) Two-way interaction of input variables in the sensitivity analysis of neural network models. Ecol Model 195: 43-50.
22. Goh ATC (1995) Back-propagation neural networks for modeling complex systems. Artif Intell Eng 9: 143-151.
23. Hagan MT, Demuth HB, Beale MH (1996) Neural network design. Campus Publishing Service, Colorado University Bookstore, Colorado. ISBN 0-9717321-0-8.
24. He F, Sung AH, Guo B (1997) A neural network for prediction of oil well cement bonding quality. In: proceedings of IASTED international conference on control, IASTED-ACTA Press, Cancun-Mexico, pp 417-420.
25. Hunter A, Kennedy L, Henry J, Ferguson I (2000) Application of neural networks and sensitivity analysis to improved prediction of trauma survival. Comput Methods Progr Biomed 62: 11-19.
26. Hornik K, Stichcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2: 359-366.
27. Kemp SL, Zaradic P, Hansen F (2007) An approach for determining relative input parameter importance and significance in artificial neural networks. Ecol Model 204: 326-334.
28. Kewley R, Embrechts M, Breneman C (2000) Data strip mining for the virtual design of pharmaceuticals with neural networks. IEEE Trans Neural Netw 11(3): 668-679.
29. Lek S, Beland A, Dimopoulos I, Lauga J, Moreau J (1995) Improved estimation, using neural networks, of the food consumption of fish populations. Mar Freshw Res 46(8): 1229-1236.
30. Lek S, Delacoste M, Baran P, Dimopoulos I, Lauga J, Aulagnier S (1996) Application of neural networks to modeling nonlinear relationships in ecology. Ecol Model 90: 39-52.
31. Lek S, Beland A, Baran P, Dimopoulos I, Delacoste M (1996) Role of some environmental variables in trout abundance models using neural networks. Aquat Living Resour 9: 23-29.
32. Lin Y, Cunningham GA (1995) A new approach to fuzzy-neural system modeling. IEEE Trans Fuzzy Syst 3(2): 190-198.
33. Martín del Bío B, Sanz Molina A (2006) Neural networks and fuzzy systems. Editorial RA-MA.
34. Masters T (1993) Practical neural networks recipes in C ++. Academic Press, Waltham.
35. Moghaddam FR, Afandizadeh S, Ziyadi M (2010) Prediction of accident severity using artificial neural networks. Int J Civ Eng 9: 1.
36. Mohammadipour AH, Alavi SH (2009) The optimization of the geometric cross-section dimensions of raised pedestrian crosswalks: a case of study in Qazvin. Accid Anal Prev 41: 314-326.
37. Mussone L, Ferrari A, Oneta M (1999) An analysis of urban collisions using an artificial intelligence model. Accid Anal Prev 31: 705-718.
38. Olden JD, Jackson DA (2002) Illuminating the "black-box": a randomization approach for understanding variable contributions in artificial neural networks. Ecol Model 154: 135-150.
39. Olden JD, Joy MK, Death RG (2004) An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecol Model 178: 389-397.
40. Özesmi SL, Özesmi U (1999) An artificial neural network approach to spatial habitat modeling with interspecific interaction. Ecol Model 116: 15-31.
41. Paliwal M, Kumar UA (2011) Assessing the contribution of variables in feed forward neural network. Appl Soft Comput 11: 3690-3696.
42. Palmer A, Montaño JJ (2002a) Redes neuronales artificiales aplicadas al análisis de datos. Doctoral Dissertation. University of Palma de Mallorca.
43. Palmer A, Montaño JJ (2002) Redes neuronales artificiales: abriendo la caja negra. Metodología de las ciencias del comportamiento 4(1): 77-93.
44. Palmer A, Montaño JJ (2003) Numeric sensitivity analysis applied to feed forward neural networks. Neural Comput Appl 12: 119-125.
45. Rzempoluk EJ (1998) Neural network data analysis using simulnet. Springer, New York.
46. Rumelhart DE, McClelland JL (1986) Parallel distributed processing, vol 1: foundations. MIT Press, Cambridge.
47. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by backpropagation errors. Nature 323: 533-536.
48. Scardi M, Harding LW (1999) Developing an empirical model of phytoplankton primary production: a neural networks case study. Ecol Model 120(2-3): 213-223.
49. Sung AH (1998) Ranking importance of input parameters of neural networks. Expert Syst Appl 15: 405-411.
50. Watts MJ, Worner SP (2008) Using artificial neural networks to determine the relative contribution of abiotic factors influencing the establishment of insect pest species. Ecol Inform 3: 64-74.
51. Werbos PJ (1974) Beyond regression: new tools for prediction and analysis in behavioral sciences. Doctoral Dissertation. Applied Mathematics, Harvard University.
52. Yao J, Teng N, Poh HL, Tan CL (1998) Forecasting and analysis of marketing data using neural networks. J Inf Sci Eng 14: 843-862.
53. Yeh I, Cheng W (2010) First and second order sensitivity analysis of MLP. Neurocomputing 73: 2225-2233.
54. Zhou ZH, Wu J, Tang W (2002) Ensembling neural networks: many could be better than all. Artif Intell 137(1-2): 239-263.
55. Zurada JM, Malinowski A, Cloete I (1994) Sensitivity analysis for minimization of input data dimensión for feed forward neural network. In: proceedings of IEEE international symposium on circuits and systems, IEEE Press, London.