메뉴 건너뛰기




Volumn 36, Issue 1, 2012, Pages 61-74

A robust missing value imputation method for noisy data

Author keywords

Group method of data handling (GMDH); Missing data imputation; Noise

Indexed keywords

BENCHMARK DATASETS; GROUP METHOD OF DATA HANDLING; IMPACT OF NOISE; IMPUTATION METHODS; IMPUTATION TECHNIQUES; INCOMPLETE DATA; MISSING DATA; MISSING VALUE IMPUTATION; NOISE; NOISY DATA; REAL WORLD DATA; RESEARCH TOPICS;

EID: 84856282936     PISSN: 0924669X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10489-010-0244-1     Document Type: Article
Times cited : (49)

References (50)
  • 1
    • 28744437197 scopus 로고    scopus 로고
    • GMDH-based feature ranking and selection for improved classification of medical data
    • DOI 10.1016/j.jbi.2005.03.003, PII S153204640500033X
    • RE Abdel-Aal 2005 GMDH-based feature ranking and selection for improved classification of medical data J Biomed Inf 38 6 456 468 10.1016/j.jbi.2005.03. 003 (Pubitemid 41758095)
    • (2005) Journal of Biomedical Informatics , vol.38 , Issue.6 , pp. 456-468
    • Abdel-Aal, R.E.1
  • 2
    • 0024045475 scopus 로고
    • A characterisation at unbiased structure and conditions of their J-optimality
    • 0706.62064
    • TI Aksenova YP Yurachkovsky 1988 A characterisation at unbiased structure and conditions of their J-optimality Sov J Autom Inf Sci 21 4 36 42 0706.62064
    • (1988) Sov J Autom Inf Sci , vol.21 , Issue.4 , pp. 36-42
    • Aksenova, T.I.1    Yurachkovsky, Y.P.2
  • 3
    • 36948999941 scopus 로고    scopus 로고
    • Irvine, CA: University of California, School of Information and Computer Science
    • Asuncion A, Newman DJ (2007) UCI machine learning repository. Irvine, CA: University of California, School of Information and Computer Science. http://www.ics.uci.edu/mlearn/MLRepository.html
    • (2007) UCI Machine Learning Repository
    • Asuncion, A.1    Newman, D.J.2
  • 4
    • 67049165534 scopus 로고    scopus 로고
    • A conservative feature subset selection algorithm with missing data
    • Kellenberger P (ed) Pisa, Italy
    • Aussem A, de Morais SR (2008) A conservative feature subset selection algorithm with missing data. In: Kellenberger P (ed) Proc eighth IEEE int conf on data mining, ICDM'08, Pisa, Italy, pp 725-730
    • (2008) Proc Eighth IEEE Int Conf on Data Mining, ICDM'08 , pp. 725-730
    • Aussem, A.1    De Morais, S.R.2
  • 6
    • 0242498488 scopus 로고    scopus 로고
    • An analysis of four missing data treatment methods for supervised learning
    • 10.1080/713827181
    • G Batista MC Monard 2003 An analysis of four missing data treatment methods for supervised learning Appl Artif Intell 17 5-6 519 533 10.1080/713827181
    • (2003) Appl Artif Intell , vol.17 , Issue.56 , pp. 519-533
    • Batista, G.1    Monard, M.C.2
  • 7
    • 13244283767 scopus 로고    scopus 로고
    • On regression imputation in the presence of nonignorable nonresponse
    • Beaumont JF (2000) On regression imputation in the presence of nonignorable nonresponse. In: Proc of the survey research methods section, ASA, pp 580-585
    • (2000) Proc of the Survey Research Methods Section, ASA , pp. 580-585
    • Beaumont, J.F.1
  • 8
    • 0042880013 scopus 로고    scopus 로고
    • Generating weighted fuzzy rules from relational database systems for estimating null values using genetic algorithms
    • 10.1109/TFUZZ.2003.814837
    • S Chen C Huang 2003 Generating weighted fuzzy rules from relational database systems for estimating null values using genetic algorithms IEEE Trans Fuzzy Syst 11 4 495 506 10.1109/TFUZZ.2003.814837
    • (2003) IEEE Trans Fuzzy Syst , vol.11 , Issue.4 , pp. 495-506
    • Chen, S.1    Huang, C.2
  • 9
    • 44949088574 scopus 로고    scopus 로고
    • A new approach to generate weighted fuzzy rules using genetic algorithms for estimating null values
    • 10.1016/j.eswa.2007.07.033
    • S Chen C Huang 2008 A new approach to generate weighted fuzzy rules using genetic algorithms for estimating null values Expert Syst Appl 35 3 905 917 10.1016/j.eswa.2007.07.033
    • (2008) Expert Syst Appl , vol.35 , Issue.3 , pp. 905-917
    • Chen, S.1    Huang, C.2
  • 10
    • 0002629270 scopus 로고
    • Maximum likelihood from incomplete data via the em algorithm (with discussion)
    • 0364.62022 501537
    • AP Dempster NM Laird DB Rubin 1977 Maximum likelihood from incomplete data via the EM algorithm (with discussion) J R Stat Soc B 39 1 38 0364.62022 501537
    • (1977) J R Stat Soc B , vol.39 , pp. 1-38
    • Dempster, A.P.1    Laird, N.M.2    Rubin, D.B.3
  • 13
    • 49649098733 scopus 로고    scopus 로고
    • Impact of imputation of missing values on classification error for discrete data
    • 10.1016/j.patcog.2008.05.019 1173.68479
    • A Farhangfar L Kurgan J Dy 2008 Impact of imputation of missing values on classification error for discrete data Pattern Recogn 41 12 3692 3705 10.1016/j.patcog.2008.05.019 1173.68479
    • (2008) Pattern Recogn , vol.41 , Issue.12 , pp. 3692-3705
    • Farhangfar, A.1    Kurgan, L.2    Dy, J.3
  • 14
    • 0000238915 scopus 로고
    • An overview of hot-deck procedures
    • W.G. Madow I. OIkin D.B. Rubin (eds). Academic Press New York
    • Ford BL (1983) An overview of hot-deck procedures. In: Madow WG, OIkin I, Rubin DB (eds) Incomplete data in sample surveys, vol II: theory and bibliographies. Academic Press, New York, pp 85-207
    • (1983) Incomplete Data in Sample Surveys, Vol II: Theory and Bibliographies , pp. 85-207
    • Ford, B.L.1
  • 15
    • 34250686456 scopus 로고    scopus 로고
    • Multiple imputation: Review of theory, implementation and software
    • DOI 10.1002/sim.2787
    • O Harel XH Zhou 2007 Multiple imputation: Review of theory, implementation and software Stat Med 26 16 3057 3077 10.1002/sim.2787 2380504 (Pubitemid 47019007)
    • (2007) Statistics in Medicine , vol.26 , Issue.16 , pp. 3057-3077
    • Harel, O.1    Zhou, X.-H.2
  • 16
    • 0036132613 scopus 로고    scopus 로고
    • Clustering incomplete relational data using the non-Euclidean relational fuzzy c-means algorithm
    • DOI 10.1016/S0167-8655(01)00115-5, PII S0167865501001155
    • RJ Hathaway JC Bezdek 2002 Clustering incomplete relational data using the non-Euclidean relational fuzzy c-means algorithm Pattern Recogn Lett 23 1-3 151 160 10.1016/S0167-8655(01)00115-5 0993.68108 (Pubitemid 33119508)
    • (2002) Pattern Recognition Letters , vol.23 , Issue.1-3 , pp. 151-160
    • Hathaway, R.J.1    Bezdek, J.C.2
  • 18
    • 3543068706 scopus 로고    scopus 로고
    • A grey-based nearest neighbor approach for missing attribute value prediction
    • 10.1023/B:APIN.0000021416.41043.0f 1069.68577
    • CC Huang HM Lee 2004 A grey-based nearest neighbor approach for missing attribute value prediction Appl Intell 20 239 252 10.1023/B:APIN.0000021416. 41043.0f 1069.68577
    • (2004) Appl Intell , vol.20 , pp. 239-252
    • Huang, C.C.1    Lee, H.M.2
  • 19
    • 0001784588 scopus 로고
    • The group method of data handling-a rival of the method of stochastic approximation
    • AG Ivakhnenko 1968 The group method of data handling-a rival of the method of stochastic approximation Sov Autom Control 1-3 43 55
    • (1968) Sov Autom Control , vol.13 , pp. 43-55
    • Ivakhnenko, A.G.1
  • 20
    • 0015142058 scopus 로고
    • Polynomial theory of complex systems
    • 10.1109/TSMC.1971.4308320 309583
    • AG Ivakhnenko 1971 Polynomial theory of complex systems IEEE Trans Syst Man Cybern 1 4 364 378 10.1109/TSMC.1971.4308320 309583
    • (1971) IEEE Trans Syst Man Cybern , vol.1 , Issue.4 , pp. 364-378
    • Ivakhnenko, A.G.1
  • 21
    • 0020851141 scopus 로고
    • Theory of two-level gmdh algorithms for long-range quantitative prediction
    • AG Ivakhnenko YL Kocherga 1983 Theory of two-level GMDH algorithms for long-range quantitative prediction Sov Autom Control 16 6 7 12 (Pubitemid 14647063)
    • (1983) Soviet automatic control , vol.16 , Issue.6 , pp. 7-12
    • Ivakhnenko, A.G.1    Kocherga, Yu.L.2
  • 23
    • 0033225727 scopus 로고    scopus 로고
    • Imputation of missing data in industrial databases
    • DOI 10.1023/A:1008334909089
    • K Lakshminarayan SA Harp T Samad 1999 Imputation of missing data in industrial databases Appl Intell 11 3 259 275 10.1023/A:1008334909089 (Pubitemid 30517149)
    • (1999) Applied Intelligence , vol.11 , Issue.3 , pp. 259-275
    • Lakshminarayan, K.1    Harp, S.A.2    Samad, T.3
  • 24
    • 33746342093 scopus 로고    scopus 로고
    • Self-organising data mining
    • DOI 10.1080/0232929031000136135, PII 8WTKCERDE1GF1TFU
    • F Lemke J Mueller 2003 Self-organising data mining Syst Anal Model Simul 43 2 231 240 10.1080/0232929031000136135 (Pubitemid 44112342)
    • (2003) Systems Analysis Modelling Simulation , vol.43 , Issue.2 , pp. 231-240
    • Lemke, F.1    Muller, J.-A.2
  • 27
    • 15544363963 scopus 로고    scopus 로고
    • Building bayesian network models in medicine: The MENTOR experience
    • DOI 10.1007/s10489-005-5599-3
    • S Mani M Valtorta S McDermott 2005 Building Bayesian network models in medicine: The MENTOR experience Appl Intell 22 2 93 108 10.1007/s10489-005-5599- 3 (Pubitemid 40400728)
    • (2005) Applied Intelligence , vol.22 , Issue.2 , pp. 93-108
    • Mani, S.1    Valtorta, M.2    McDermott, S.3
  • 28
    • 58749104155 scopus 로고    scopus 로고
    • Classification algorithm sensitivity to training data with non representative attribute noise
    • 10.1016/j.dss.2008.11.021
    • M Mannino Y Yang Y Ryu 2009 Classification algorithm sensitivity to training data with non representative attribute noise Decis Support Syst 46 3 743 751 10.1016/j.dss.2008.11.021
    • (2009) Decis Support Syst , vol.46 , Issue.3 , pp. 743-751
    • Mannino, M.1    Yang, Y.2    Ryu, Y.3
  • 29
    • 60549115783 scopus 로고    scopus 로고
    • Investigating the efficiency in oil futures market based on GMDH approach
    • 10.1016/j.eswa.2008.09.055
    • M Mehrara, et al. 2009 Investigating the efficiency in oil futures market based on GMDH approach Expert Syst Appl 36 4 7479 7483 10.1016/j.eswa.2008.09. 055
    • (2009) Expert Syst Appl , vol.36 , Issue.4 , pp. 7479-7483
    • Mehrara, M.1
  • 32
    • 0035506257 scopus 로고    scopus 로고
    • Analyzing data sets with missing data: An empirical evaluation of imputation methods and likelihood-based methods
    • DOI 10.1109/32.965340
    • I Myrtveit E Stensrud U Olsson 2001 Analyzing data sets with missing data: An empirical evaluation of imputation methods and likelihood-based methods IEEE Trans Softw Eng 27 11 999 1013 10.1109/32.965340 (Pubitemid 33106028)
    • (2001) IEEE Transactions on Software Engineering , vol.27 , Issue.11 , pp. 999-1013
    • Myrtveit, I.1    Stensrud, E.2    Olsson, U.H.3
  • 33
    • 0036529715 scopus 로고    scopus 로고
    • The design of self-organizing Polynomial Neural Networks
    • DOI 10.1016/S0020-0255(02)00175-5, PII S0020025502001755
    • S Oh W Pedrycz 2002 The design of self-organizing polynomial neural networks Inf Sci 141 3-4 237 258 10.1016/S0020-0255(02)00175-5 1008.68098 (Pubitemid 34524130)
    • (2002) Information Sciences , vol.141 , Issue.3-4 , pp. 237-258
    • Oh, S.-K.1    Pedrycz, W.2
  • 34
    • 0041589601 scopus 로고    scopus 로고
    • The comparative efficacy of imputation methods for missing data in structural equation modeling
    • 10.1016/S0377-2217(02)00578-7 1113.62361 1997008
    • A Olinsky S Chen L Harlow 2003 The comparative efficacy of imputation methods for missing data in structural equation modeling Eur J Oper Res 151 1 53 79 10.1016/S0377-2217(02)00578-7 1113.62361 1997008
    • (2003) Eur J Oper Res , vol.151 , Issue.1 , pp. 53-79
    • Olinsky, A.1    Chen, S.2    Harlow, L.3
  • 35
    • 34548755547 scopus 로고    scopus 로고
    • A GMDH neural network-based approach to passive robust fault detection using a constraint satisfaction backward test
    • DOI 10.1016/j.engappai.2006.12.005, PII S0952197606002284
    • V Puig, et al. 2007 A GMDH neural network-based approach to passive robust fault detection using a constraint satisfaction backward test Eng Appl Artif Intell 20 7 886 897 10.1016/j.engappai.2006.12.005 (Pubitemid 47432842)
    • (2007) Engineering Applications of Artificial Intelligence , vol.20 , Issue.7 , pp. 886-897
    • Puig, V.1    Witczak, M.2    Nejjari, F.3    Quevedo, J.4    Korbicz, J.5
  • 36
    • 34250704657 scopus 로고    scopus 로고
    • Semi-parametric optimization for missing data imputation
    • DOI 10.1007/s10489-006-0032-0
    • Y Qin, et al. 2007 Semi-parametric optimization for missing data imputation Appl Intell 27 1 79 88 10.1007/s10489-006-0032-0 1189.68124 (Pubitemid 46960845)
    • (2007) Applied Intelligence , vol.27 , Issue.1 , pp. 79-88
    • Qin, Y.1    Zhang, S.2    Zhu, X.3    Zhang, J.4    Zhang, C.5
  • 38
    • 0017133178 scopus 로고
    • Inference and missing data
    • 10.1093/biomet/63.3.581 0344.62034 455196
    • DB Rubin 1976 Inference and missing data Biometrika 63 3 581 592 10.1093/biomet/63.3.581 0344.62034 455196
    • (1976) Biometrika , vol.63 , Issue.3 , pp. 581-592
    • Rubin, D.B.1
  • 40
    • 0032960273 scopus 로고    scopus 로고
    • Multiple imputation: A primer
    • DOI 10.1191/096228099671525676
    • JL Schafer 1999 Multiple imputation: A primer Stat Methods Med Res 8 1 3 15 10.1191/096228099671525676 (Pubitemid 29222784)
    • (1999) Statistical Methods in Medical Research , vol.8 , Issue.1 , pp. 3-15
    • Schafer, J.L.1
  • 41
    • 0022755410 scopus 로고
    • The present state of the theory of the group method of data handling
    • 0636.93072
    • VS Stepashko YP Yurachkovskiy 1986 The present state of the theory of the group method of data handling Sov J Autom Inf Sci c/c of Avtomatika 19 4 36 46 0636.93072
    • (1986) Sov J Autom Inf Sci C/c of Avtomatika , vol.19 , Issue.4 , pp. 36-46
    • Stepashko, V.S.1    Yurachkovskiy, Y.P.2
  • 43
    • 27644559357 scopus 로고    scopus 로고
    • A review of techniques for treating missing data in OM survey research
    • DOI 10.1016/j.jom.2005.03.001, PII S027269630500077X
    • N Tsikriktsis 2005 A review of techniques for treating missing data in OM survey research J Oper Manag 24 1 53 62 (Pubitemid 41570132)
    • (2005) Journal of Operations Management , vol.24 , Issue.1 , pp. 53-62
    • Tsikriktsis, N.1
  • 44
    • 67651230252 scopus 로고    scopus 로고
    • An empirical comparison of techniques for handling incomplete data when using decision trees
    • 10.1080/08839510902872223
    • B Twala 2009 An empirical comparison of techniques for handling incomplete data when using decision trees Appl Artif Intell 23 5 373 405 10.1080/08839510902872223
    • (2009) Appl Artif Intell , vol.23 , Issue.5 , pp. 373-405
    • Twala, B.1
  • 45
    • 9644300921 scopus 로고    scopus 로고
    • Development of pedotransfer functions using a group method of data handling for the soil of the Pianura Padano-Veneta region of North Italy: Water retention properties
    • DOI 10.1016/j.geoderma.2004.05.007, PII S0016706104001247
    • F Ungaro C Calzolari E Busoni 2005 Development of pedotransfer functions using a group method of data handling for the soil of the Pianura Padano-Veneta region of North Italy: Water retention properties Geoderma 124 3-4 293 317 10.1016/j.geoderma.2004.05.007 (Pubitemid 39568972)
    • (2005) Geoderma , vol.124 , Issue.3-4 , pp. 293-317
    • Ungaro, F.1    Calzolari, C.2    Busoni, E.3
  • 47
    • 40749151020 scopus 로고    scopus 로고
    • A comprehensive empirical evaluation of missing value imputation in noisy software measurement data
    • DOI 10.1016/j.jss.2007.07.043, PII S0164121207002397
    • J Van Hulse TM Khoshgoftaar 2008 A comprehensive empirical evaluation of missing value imputation in noisy software measurement data J Syst Softw 81 5 691 708 (Pubitemid 351389565)
    • (2008) Journal of Systems and Software , vol.81 , Issue.5 , pp. 691-708
    • Van Hulse, J.1    Khoshgoftaar, T.M.2
  • 49
    • 46649091716 scopus 로고    scopus 로고
    • Mining with noise knowledge: Error-aware data mining
    • 10.1109/TSMCA.2008.923034
    • X Wu X Zhu 2008 Mining with noise knowledge: Error-aware data mining IEEE Trans Syst Man Cybern Part A 38 4 917 932 10.1109/TSMCA.2008.923034
    • (2008) IEEE Trans Syst Man Cybern Part A , vol.38 , Issue.4 , pp. 917-932
    • Wu, X.1    Zhu, X.2
  • 50
    • 19544372918 scopus 로고    scopus 로고
    • Class noise vs. attribute noise: A quantitative study
    • 10.1007/s10462-004-0751-8 1069.68587 2083952
    • X Zhu X Wu 2004 Class noise vs. attribute noise: A quantitative study Artif Intell Rev 22 3 177 210 10.1007/s10462-004-0751-8 1069.68587 2083952
    • (2004) Artif Intell Rev , vol.22 , Issue.3 , pp. 177-210
    • Zhu, X.1    Wu, X.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.