Effects of the neural network s-Sigmoid function on KDD in the presence of imprecise data

Computers and Operations Research

Volumn 33, Issue 11, 2006, Pages 3136-3149

  Kros, John F ^a   Lin, Mike ^b   Brown, Marvin L ^c  

^a EAST CAROLINA UNIVERSITY   (United States)

^b Cleveland State University   (United States)

^c GRAMBLING STATE UNIVERSITY   (United States)

Author keywords

Data mining; Imputation; KDD; Neural networks

Indexed keywords

ALGORITHMS; DATA ACQUISITION; DATA MINING; KNOWLEDGE ACQUISITION; NEURAL NETWORKS; PATTERN RECOGNITION;

ASSOCIATION RULES; IMPUTATION; KNOWLEDGE DISCOVERY OF DATABASES (KDD);

DATABASE SYSTEMS;

EID: 33644691638     PISSN: 03050548     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cor.2005.01.024     Document Type: Article

References (45)

1. Jordan MI. Why the logistic function? A tutorial discussion on probabilities and neural networks. Computational Cognitive Science Technical Report 9503. Cambridge, MA: Massachusetts Institute of Technology; 1995.
2. M. Berry, and G. Linoff Data mining techniques 1997 Wiley New York
3. T.M. Mitchell Machine learning 1997 WCB/McGraw-Hill Boston
4. L.P.J. Veelenturf Analysis and applications of artificial neural networks 1995 Prentice-Hall New York
5. A. Afifi, and R. Elashoff Missing observations in multivariate statistics I review of the literature Journal of the American Statistical Association 61 1966 595 604
6. H. Hartley, and R. Hocking The analysis of incomplete data. Biometrics 27 1971 783 808
7. Orchard T, Woodbury M. A missing information principle: theory and applications. Proceedings of the sixth Berkeley symposium on mathematical statistics and probability, vol. 1. 1972. p. 697-715.
8. A. Dempster, N. Laird, and D. Rubin Maximum likelihood from incomplete data via the EM algorithm (with discussion) Journal of the Royal Statistical Society B39 1977 1 38
9. R. Little Models for nonresponse in sample surveys Journal of the American Statistical Association 77 1982 237 250
10. R. Little, and D. Rubin Statistical analysis with missing data 1987 Wiley New York
11. P. Diggle, and M. Kenward Informative dropout in longitudinal data analysis (with discussion) Applied Statistics 43 1994 49 94
12. J. Graham, S. Hofer, S. Donaldson, D. MacKinnon, and J. Schafer Analysis with missing data in prevention research K. Bryant W. Windle S. West New methodological approaches to alcohol prevention research 1997 American Psychological Association Washington, DC
13. R. Little Modeling the drop-out mechanism in repeated-measures studies Journal of the American Statistical Association 90 1995 1112 1121
14. R. Little, and D. Rubin The analysis of social science data with missing values Sociological Methods and Research 18 1989 292 326
15. Howell DC. Treatment of missing data. D. C. Howell personal website, 1998. Available http://www.uvm.edu/ ∼ dhowell/StatPages/More_Stuff/Missing_Data/Missing.html/
16. J. Schafer Analysis of incomplete multivariate data 1997 Chapman & Hall London
17. Little R. Regression with missing X's: a review. Journal of the American Statistical Association 1992; 87:1227-37.
18. Rubin D. Multiple imputations in sample surveys - a phenomenological Bayesian approach to nonresponse. Imputation and Editing of Faulty or Missing Survey Data, US Department of Commerce, 1978. p. 1-23.
19. Kim Y. The curse of the missing data. Y. Kim personal website, 2001. Available http://209.68.240.11:8080/2ndMoment/978476655/addPostingForm/
20. Statistical Services of University of Texas. General FAQ #25: handling missing or incomplete data, 2000. Available http://www.utexas.edu/cc/faqs/stat/general/gen25.html/
21. Graham JW, Hofer SM, Piccinin AM. Analyses with missing data in drug abuse prevention research. In: Collins LM, Seitz LA, editors. Drug abuse prevention intervention research: methodological issues. National Institute on Drug Abuse Research Monograph Series, Washington, DC: NIDA; 1994. p. 13-63.
22. N. Nie, C. Hull, J. Jenkins, K. Steinbrenner, and D. Bent SPSS 2nd ed. 1975 McGraw-Hill New York
23. D. Rubin Multiple imputation after 18+ years (with discussion) Journal of the American Statistical Association 91 1996 473 489
24. J. Schafer Multiple imputation a primer Statistical Methods in Medical Research 8 1999 3 15
25. J. Schafer, and M. Olsen Multiple imputation for multivariate missing-data problems a data analyst's perspective Multivariate Behavioral Research 33 1998 545 571
26. J. Barnard, and X. Meng Applications of multiple imputation in medical studies from AIDS to NHANES Statistical Methods in Medical Research 8 1999 17 36
27. S. van Buren, H. Boshuizen, and D. Knook Multiple imputation of missing blood pressure covariates in survival analysis Statistics in Medicine 18 1999 681 694
28. C. Clogg, D. Rubin, N. Schenker, B. Schultz, and L. Weidman Multiple imputation of industry and occupation codes in census public-use samples using Bayesian logistic regression Journal of the American Statistical Association 86 413 1991 68 78
29. Ernst L. Variance of the estimated mean for several imputation procedures. American Statistical Association 1980, Proceedings of the Survey Research Methods Section, 1980. p. 716-20.
30. Kalton G, Kish L. Two efficient random imputation procedures. American Statistical Association 1981, Proceedings of the survey research methods section, 1981. p. 146-51.
31. Ford B. An overview of hot deck procedures. In: Madow WG, Olkin I, Rubin D, editors. Sample surveys, vol. II: Theory and annotated bibliography. New York: Academic Press; 1981. p. 3-10.
32. M. David, R. Little, M. Samuhel, and R. Triest Alternative methods for CPS income imputation Journal of the American Statistical Association 81 1986 29 41
33. J. Hair, R. Anderson, R. Tatham, and W. Black Multivariate data analysis 1998 Prentice-Hall Upper Saddle River, NJ
34. Dempster A, Rubin D. Incomplete data in sample surveys. In: Madow WG, Olkin I, Rubin D, editors. Sample surveys, vol. II: Theory and annotated bibliography. New York: Academic Press; 1983. p. 3-10.
35. J.G. Kovar, and P. Whitridge Imputation of business survey data B.G. Cox Business survey methods 1995 Wiley New York 403 423
36. P. Adriaans, and D. Zantinge Data mining 1997 Addison-Wesley New York
37. S. Haykin Neural networks a comprehensive foundation 1994 Macmillan Publishing New York
38. Masters. Neural, novel, and hybrid algorithms for time series predictions 1995 Wiley New York
39. B. Ripley Pattern recognition and neural networks 1996 Cambridge University Press Cambridge, UK
40. B. Warner, and M. Misra Understanding neural networks as statistical tools The American Statistician 50 1996 284 293
41. Z. Ghahramani, and M. Jordan Mixture models for learning form incomplete data J. Cowan G. Tesauro J. Alspector Advances in neural information processing systems 6 1997 Morgan Kaufman San Mateo, CA 120 127
42. V. Tresp, R. Neuneier, and S. Ahmad Efficient methods for dealing with missing data in supervised learning G. Tesauro D. Touretzky T. Keen Advances in neural information processing systems 7 1995 The MIT Press Cambridge, MA 689 696
43. C. Westphal, and T. Blaxton Data mining solutions 1998 Wiley New York
44. R. Groth Data mining building competitive advantage 2000 Prentice-Hall Upper Saddle River, NJ
45. R. Roiger, and M. Geatz Data mining a tutorial based primer 2002 Pearson Education New York