Pattern classification with missing data: a review

Neural Computing and Applications - Tập 19 - Trang 263-282 - 2009
Pedro J. García-Laencina1, José-Luis Sancho-Gómez1, Aníbal R. Figueiras-Vidal2
1Dpto. Tecnologías de la Información y las Comunicaciones, Universidad Politécnica de Cartagena, Cartagena (Murcia), Spain
2Dpto. Teoría de Señal y Comunicaciones, Universidad Carlos III de Madrid, Leganés (Madrid), Spain

Tóm tắt

Pattern classification has been successfully applied in many problem domains, such as biometric recognition, document classification or medical diagnosis. Missing or unknown data are a common drawback that pattern recognition techniques need to deal with when solving real-life classification tasks. Machine learning approaches and methods imported from statistical learning theory have been most intensively studied and used in this subject. The aim of this work is to analyze the missing data problem in pattern classification tasks, and to summarize and compare some of the well-known methods used for handling missing values.

Tài liệu tham khảo

Duda RO, Hart PE, Stork DG (2000) Pattern classification. Wiley, New York Ripley BD (1996) Pattern recognition and neural networks. Cambridge University Press, New York Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford Watanabe S (1985) Pattern recognition: human and mechanical. Wiley, New York Jain AK, Duin RPW, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22(1):4–37 Little RJA, Rubin DB (2002) Statistical analysis with missing data, 2nd edn. Wiley, New Jersey Schafer JL (1997) Analysis of incomplete multivariate data. Chapman & Hall, Florida Allison PD (2001) Missing data. Sage university papers series on quantitative applications in the social sciences. Thousan Oaks, California Rubin DB (1987) Multiple imputation for nonresponse in surveys. Wiley, New York Wang L, Fan X (2004) Missing data in disguise and implications for survey data analysis. Field Methods 16(3):332–351 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 Lakshminarayan K, Harp SA, Samad T (2004) Imputation of missing data in industrial databases. Eng Appl Artif Intell 11(3):259–275 Ji C, Elwalid A (2000) Measurement-based network monitoring: missing data formulation and scalability analysis. IEEE Int Symp Inf Theory, Sorrento, Italy, p 78 Halatchev M, Gruenwald L (2005) Estimating missing values in related sensor data streams. In Int Conf Manage Data, pp 83–94 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 Cooke M, Green P, Crawford M (1994) Handling missing data in speech recognition. Int Conf Spoken Lang Process, pp 1555–1558 Parveen S, Green P (2004) Speech enhancement with missing data techniques using recurrent neural networks. In: IEEE ICASSP, vol 1, pp 733–736 DiCesare G (2006) Imputation, estimation and missing data in finance. Ph.D. dissertation, University of Waterloo Sharpe IG, Kofman P (2003) Using multiple imputation in the analysis of incomplete observations in finance. J Financ Econ 1(2):216–249 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 Kim H, Golub GH, Park H (2004) Imputation of missing values in DNA microarray gene expression data. In: Proc IEEE Comput Syst Bioinform Conf 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 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 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 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 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 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 Kohonen T (2006) Self-organizing maps, 3rd edn. Springer Samad T, Harp SA (1992) Self-organization with partial data. Netw Computat Neural Syst 3(2):205–212 Fessant F, Midenet S (2002) Self-organizing map for data imputation and correction in surveys. Neural Comput Appl 10(4):300–310 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 Sharpe PK, Solly RJ (1995) Dealing with missing values in neural network-based diagnostic systems. Neural Comput Appl 3(2):73–77 Nordbotten S (1996) Neural network imputation applied to the Norwegian 1990 population census data. J Off Stat 12:385–401 Gupta A, Lam MS (1996) Estimating missing values using neural networks. J Oper Res Soc 47(2):229–238 Yoon SY, Lee SY (1999) Training algorithm with incomplete data for feed-forward neural networks. Neural Process Lett 10:171–179 Kallin L (2002) Missing data and the preprocessing perceptron. Tech. Rep., Umeaå University 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 Parveen S (2003) Connectionist approaches to the deployment of prior knowledge for improving robustness in automatic speech recognition. Ph.D. dissertation, University of Sheffield Pyle D (1999) Data preparation for data mining. Morgan Kaufmann Publishers Inc., San Francisco 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 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 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 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 Caruana R (1997) Multitask learning. Ph.D. dissertation, Carnegie Mellon University Silver DL (2000) Selective transfer of neural network task knowledge, Ph.D. dissertation, University of Western Ontario 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 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 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 Ghahramani Z, Jordan MI (1994) Learning from incomplete data. Tech. Rep. AIM-1509, Massachusetts Institute of Technology, Cambridge, MA, USA McLachlan GJ, Krishnan T (1997) The EM algorithm and extensions. Wiley, New York 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 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 Tresp V, Neuneier R, Ahmad S (1994) Efficient methods 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 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 Ramoni M, Sebastiani P (2001) Robust learning with missing data. Mach Learn 45:147–170 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 Jian K, Chen H, Yuan S (2005) Classification for incomplete data using classifier ensembles. In: Proc Intl Conf Neural Netw Brain, pp 559–563 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 Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81–106 Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann (Series in Machine Learning) 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 Webb GI (1998) The problem of missing values in decision tree grafting. In: Proc Aust Jt Conf Artif Intell, Springer, pp 273–283 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 Clark P, Niblett T (1989) The CN2 induction algorithm. Mach Learn 3(4):261–283 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 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 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 Petit-Renaud S, Denux T (1998) A neuro-fuzzy model for missing data reconstruction. In: Proc IEEE Workshop Emerg Technol, St. Paul, MN, USA Gabrys B (2000) Pattern classification for incomplete data. In: Proc Intl Conf Knowl Based Intell Eng Syst Allied Technol, Brightom, UK, pp 454–457 Gabrys B (2002) Neuro-fuzzy approach to processing inputs with missing values in pattern recognition problems. Int J Approx Reason 30(3):149–179 Berthold MR, Huber KP (1998) Missing values and learning of fuzzy rules. Intl J Uncertain Fuzzy Knowl Based Syst 6(2):171–178 Berthold MR, Huber KP (1997) Missing values and learning of fuzzy rules. In: Proc Workshop Fuzzy Neuro Syst, 1997 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 Hathaway RJ, Bezdek JC (2001) Fuzzy C-means clustering of incomplete data. IEEE Trans Syst Man Cybern B Cybern 31(5):735–744 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 Sarkar M, Leong TY (2001) Fuzzy k-means clustering with missing values. In: Proc AMIA Annu Symp, pp 588–592 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 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 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 Pelckmans K, Brabanter JD, Suykens JAK, Moor BD (2005) Handling missing values in support vector machine classifiers. Neural Netw 18(5–6):684–692 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 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 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