Pattern classification with missing data: A review

Neural Computing and Applications

Volumn 19, Issue 2, 2010, Pages 263-282

  García Laencina, Pedro J ^a   Sancho Gómez, José Luis ^a   Figueiras Vidal, Aníbal R ^b  

^a TECHNICAL UNIVERSITY OF CARTAGENA   (Spain)

^b UNIVERSIDAD CARLOS III DE MADRID   (Spain)

Author keywords

Machine learning; Missing data; Neural networks; Pattern classification

Indexed keywords

BIOMETRIC RECOGNITION; DOCUMENT CLASSIFICATION; HANDLING MISSING VALUES; MACHINE LEARNING APPROACHES; MISSING DATA; MISSING DATA PROBLEM; PATTERN RECOGNITION TECHNIQUES; STATISTICAL LEARNING THEORY;

BIOMETRICS; DIAGNOSIS; LEARNING SYSTEMS; NEURAL NETWORKS; PATTERN RECOGNITION;

INFORMATION RETRIEVAL SYSTEMS;

EID: 79952229665     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-009-0295-6     Document Type: Article

References (83)

1. Duda RO, Hart PE, Stork DG (2000) Pattern classification. Wiley, New York
2. Ripley BD (1996) Pattern recognition and neural networks. Cambridge University Press, New York
3. Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford
4. Watanabe S (1985) Pattern recognition: human and mechanical. Wiley, New York
5. Jain AK, Duin RPW, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22(1):4-37
6. Little RJA, Rubin DB (2002) Statistical analysis with missing data, 2nd edn. Wiley, New Jersey
7. Schafer JL (1997) Analysis of incomplete multivariate data. Chapman & Hall, Florida
8. Allison PD (2001) Missing data. Sage university papers series on quantitative applications in the social sciences. Thousan Oaks, California
9. Rubin DB (1987) Multiple imputation for nonresponse in surveys. Wiley, New York
10. Wang L, Fan X (2004) Missing data in disguise and implications for survey data analysis. Field Methods 16(3):332-351
11. Nguyen LN, Scherer WT (2003) Imputation techniques to account for missing data in support of intelligent transportation systems applications. Tech. Rep., University of Virginia, USA
12. Lakshminarayan K, Harp SA, Samad T (2004) Imputation of missing data in industrial databases. Eng Appl Artif Intell 11(3):259-275
13. Ji C, Elwalid A (2000) Measurement-based network monitoring: missing data formulation and scalability analysis. IEEE Int Symp Inf Theory, Sorrento, Italy, p 78
14. Halatchev M, Gruenwald L (2005) Estimating missing values in related sensor data streams. In Int Conf Manage Data, pp 83-94
15. Mohammed HS, Stepenosky N, Polikar R (2006) An ensemble technique to handle missing data from sensors. In: IEEE Sens Appl Symp, Houston, Texas, USA, pp 101-105
16. Cooke M, Green P, Crawford M (1994) Handling missing data in speech recognition. Int Conf Spoken Lang Process, pp 1555-1558
17. Parveen S, Green P (2004) Speech enhancement with missing data techniques using recurrent neural networks. In: IEEE ICASSP, vol 1, pp 733-736
18. DiCesare G (2006) Imputation, estimation and missing data in finance. Ph.D. dissertation, University of Waterloo
19. Sharpe IG, Kofman P (2003) Using multiple imputation in the analysis of incomplete observations in finance. J Financ Econ 1(2):216-249
20. Troyanskaya O, Cantor M, Alter O, Sherlock G, Brown P, Botstein D, Tibshirani R, Hastie T, Altman R (2001) Missing value estimation methods for DNA microarrays. Bioinformatics 17(6):520-525
21. Kim H, Golub GH, Park H (2004) Imputation of missing values in DNA microarray gene expression data. In: Proc IEEE Comput Syst Bioinform Conf
22. Liu P, El-Darzi E, Lei L, Vasilakis C, Chountas P, Huang W (2005) An analysis of missing data treatment methods and their application to health care dataset. In: Li X et al (eds) ADMA, LNCS 3584, Springer, pp 583-590
23. Markey MK, Patel A (2004) Impact of missing data in training artificial neural networks for computer-aided diagnosis. In: Proc Int Conf Mach Learn Appl, pp 351-354
24. Proschan MA, McMahon RP, Shih JH, Hunsberger SA, Geller N, Knatterud G, Wittes J (2001) Sensitivity analysis using an imputation method for missing binary data in clinical trials. J Stat Plan Inference 96(1):155-165
25. Jerez JM, Molina I, Subirats JL, Franco L (2006) Missing data imputation in breast cancer prognosis. In BioMed'06. ACTA Press Anaheim, CA, pp 323-328
26. Batista G, Monard MC (2003) Experimental comparison of K-nearest neighbour and mean or mode imputation methods with the internal strategies used by C4.5 and CN2 to treat missing data. Tech. Rep., University of Sao Paulo
27. Batista G, Monard MC (2002) A study of K-nearest neighbour as an imputation method. In: Abraham A et al (eds) Hybrid Intell Syst, Ser Front Artif Intell Appl 87, IOS Press, pp 251-260
28. Kohonen T (2006) Self-organizing maps, 3rd edn. Springer
29. Samad T, Harp SA (1992) Self-organization with partial data. Netw Computat Neural Syst 3(2):205-212
30. Fessant F, Midenet S (2002) Self-organizing map for data imputation and correction in surveys. Neural Comput Appl 10(4): 300-310
31. Piela P (2002) Introduction to self-organizing maps modelling for imputation-techniques and technology. Res Stat Note Health Care Financ Adm Off Policy Plan Res 2:5-19
32. Sharpe PK, Solly RJ (1995) Dealing with missing values in neural network-based diagnostic systems. Neural Comput Appl 3(2):73-77
33. Nordbotten S (1996) Neural network imputation applied to the Norwegian 1990 population census data. J Off Stat 12:385-401
34. Gupta A, Lam MS (1996) Estimating missing values using neural networks. J Oper Res Soc 47(2):229-238
35. Yoon SY, Lee SY (1999) Training algorithm with incomplete data for feed-forward neural networks. Neural Process Lett 10:171-179
36. Kallin L (2002) Missing data and the preprocessing perceptron. Tech. Rep., Umeaå University
37. Bengio Y, Gingras F (1995) Recurrent neural networks for missing or asynchronous data. In: Touretzky DS et al (eds) Adv Neural Inf Process Syst 8. MIT Press, pp 395-401
38. Parveen S (2003) Connectionist approaches to the deployment of prior knowledge for improving robustness in automatic speech recognition. Ph.D. dissertation, University of Sheffield
39. Pyle D (1999) Data preparation for data mining. Morgan Kaufmann Publishers Inc., San Francisco
40. Narayanan S, Vian JL, Choi J, El-Sharkawi M, Thompson BB (2002) Set constraint discovery: missing sensor data restoration using auto-associative regression machines. In: Proc Int Jt Conf Neural Netw, Honolulu, pp 2872-2877
41. Chung D, Merat FL (1996) Neural network based sensor array signal processing. In: Proc Int Conf Multisens Fusion Integr Intell Syst, Washington, USA, pp 757-764
42. Marseguerra M, Zoia A (2005) The autoassociative neural network in signal analysis. II. Application to on-line monitoring of a simulated BWR component. Ann Nuclear Energy 32(11):1207-1223
43. Marwala T, Chakraverty S (2006) Fault classification in structures with incomplete measured data using autoassociative neural networks and genetic algorithm. Curr Sci India 90(4):542-548
44. Caruana R (1997) Multitask learning. Ph.D. dissertation, Carnegie Mellon University
45. Silver DL (2000) Selective transfer of neural network task knowledge, Ph.D. dissertation, University of Western Ontario
46. García-Laencina PJ, Figueiras-Vidal AR, Serrano-García J, Sancho-Gómez JL (2005) Exploiting multitask learning schemes using private subnetworks. In: Cabestany J et al (eds) Comput Intell Bioinsp Syst, Lect Notes Comput Sci 3512, Springer, pp 233-240
47. García-Laencina PJ, Serrano J, Figueiras-Vidal AR, Sancho-Gómez JL (2007) Multi-task neural networks for dealing with missing inputs. In: Mira J, Álvarez JR (eds) IWINAC 2007, part I, Lect Notes Comput Sci 4527, Springer, pp 282-291
48. Ghahramani Z, Jordan MI (1994) Supervised learning from incomplete data via an EM approach. In: Cowan JD et al (eds) Adv Neural Inf Process Syst 6, Morgan Kaufmann Publishers Inc., pp 120-127
49. Ghahramani Z, Jordan MI (1994) Learning from incomplete data. Tech. Rep. AIM-1509, Massachusetts Institute of Technology, Cambridge, MA, USA
50. McLachlan GJ, Krishnan T (1997) The EM algorithm and extensions. Wiley, New York
51. Ahmad S, Tresp V (1993) Some solutions to the missing feature problem in vision. In: Adv Neural Inf Process Syst 5, Morgan Kaufmann Publishers Inc., San Mateo, CA, USA, pp 393-400
52. Tresp V, Ahmad S, Neuneier R (1993) Training neural networks with deficient data. In: Cowan JD et al (eds) Adv Neural Inf Process Syst 6. Morgan Kaufmann Publishers Inc., San Francisco, pp 128-135
53. Tresp V, Neuneier R, Ahmad S (1994) Efficientmethods for dealing with missing data in supervised learning. In: Tesauro G et al (eds) Adv Neural Inf Process Syst 7, The MIT Press, pp 689-696
54. Williams D, Liao X, Xue Y, Carin L, Krishnapuram B (2007) On classification with incomplete data. IEEE Trans Pattern Anal Mach Intell 29(3):427-436
55. Ramoni M, Sebastiani P (2001) Robust learning with missing data. Mach Learn 45:147-170
56. Krause S, Polikar R (2003) An ensemble of classifiers for the missing feature problem. In: Proc Intl Jt Conf Neural Netw, Portland, USA, pp 553-558
57. Jian K, Chen H, Yuan S (2005) Classification for incomplete data using classifier ensembles. In: Proc Intl Conf Neural Netw Brain, pp 559-563
58. Juszczak P, Duin RPW (2004) Combining one-class classifiers to classify missing data. In: Roli F et al (eds) Mult Classif Syst, Lect Notes Comput Sci 3077, Springer, pp 92-101
59. Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81-106
60. Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann (Series in Machine Learning)
61. Quinlan JR (1989) Unknown attribute values in induction. In: Proc Intl Workshop Mach Learn, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 164-168
62. Webb GI (1998) The problem of missing values in decision tree grafting. In: Proc Aust Jt Conf Artif Intell, Springer, pp 273-283
63. Zheng Z, Low BT (1999) Classifying unseen cases with many missing values. In: Zhong N, Zhou L (eds) Pac Asia Conf Knowl Discov Data Min, Lect Notes Art Intell 1574, Springer, pp 370-374
64. Clark P, Niblett T (1989) The CN2 induction algorithm. Mach Learn 3(4):261-283
65. Ishibuchi H, Miyazaki A, Kwon K, Tanaka H (1993) Learning from incomplete training data with missing values and medical application. In: Proc IEEE Intl Jt Conf Neural Netw, pp 1871-1874
66. Ishibuchi H, Moriola K (1995) Classification of fuzzy input patterns by neural networks. In: Proc IEEE Intl Conf Neural Netw, Perth, WA, Australia, pp 3118-3123
67. Ishibuchi H, Tanaka H (1991) An extension of the BP-algorithm to interval input vectors-learning from numerical data and expert's knowledge. In: Proc IEEE Intl Jt Conf Neural Netw, pp 1588-1593
68. Petit-Renaud S, Denux T (1998) A neuro-fuzzy model for missing data reconstruction. In: Proc IEEE Workshop Emerg Technol, St. Paul, MN, USA
69. Gabrys B (2000) Pattern classification for incomplete data. In: Proc Intl Conf Knowl Based Intell Eng Syst Allied Technol, Brightom, UK, pp 454-457
70. Gabrys B (2002) Neuro-fuzzy approach to processing inputs with missing values in pattern recognition problems. Int J Approx Reason 30(3):149-179
71. BertholdMR, HuberKP(1998)Missing values and learning of fuzzy rules. Intl J Uncertain Fuzzy Knowl Based Syst 6(2):171-178
72. Berthold MR, Huber KP (1997) Missing values and learning of fuzzy rules. In: Proc Workshop Fuzzy Neuro Syst, 1997
73. Nauck D, Kruse R (1999) Learning in neuro-fuzzy systems with symbolic attributes and missing values. In: Proc 6th Intl Conf Neural Inf Process, Perth, WA, Australia, pp 142-147
74. Hathaway RJ, Bezdek JC (2001) Fuzzy C-means clustering of incomplete data. IEEE Trans Syst Man Cybern B Cybern 31(5): 735-744
75. Ichihashi H, Honda K (2005) Fuzzy c-means classifier for incomplete data sets with outliers and missing values. In: Proc Intl Conf Comput Intell Modell Control Autom, IEEE Computer Society, Washington, DC, USA, pp 457-464
76. Sarkar M, Leong TY (2001) Fuzzy k-means clustering with missing values. In: Proc AMIA Annu Symp, pp 588-592
77. Lim CP, Leong JH, Kuan MM (2005) A hybrid neural network system for pattern classification tasks with missing features. IEEE Trans Pattern Anal Mach Intell 27(4):648-653
78. Bhattacharyya C, Shivaswamy PK, Smola AJ (2004) A second order cone programming formulation for classifying missing data. In: Saul LK et al (eds) Adv Neural Inf Process Syst 17. MIT Press, Cambridge, pp 153-160
79. Smola AJ, Vishwanathan S, Hofmann T (2005) Kernel methods for missing variables. In: Ghahramani Z, Cowell R (eds) Proc AISTATS'05. Society for artificial intelligence and statistics, pp 325-332
80. Pelckmans K, Brabanter JD, Suykens JAK, Moor BD (2005) Handling missing values in support vector machine classifiers. Neural Netw 18(5-6):684-692
81. Bi J, Zhang T (2005) Support vector classification with input data uncertainty. In: Saul LK et al (eds) Adv Neural Inf Process Syst 17. MIT Press, Cambridge, pp 161-168
82. Chechik G, Heitz G, Elidan H, Abbeel P, Koller D (2007) Max-margin classification with incomplete data. In: Schölkopf B et al (eds) Adv Neural Inf Process Syst 19. MIT Press, Cambridge, pp 233-240
83. Kwak N, Choi C-H (2002) Input feature selection by mutual information based on Parzen window. IEEE Trans Pattern Anal Mach Intell 24(12):1667-1671