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Publicaciones Soportadas por el Proyecto KEEL
  Accountable: Julián Luengo Martín (member_email)




Main  Journal Contributions: Published

Jump to Year:
        2011  2010  2009  2008  2007  2006  2005  2004  2003  2002
        2001  1999  1998  1997



2011

  Y. Endo, Y. Hasegawa, Y. Hamasuna, Y. Kanzawa. Fuzzy c-means clustering for uncertain data using quadratic penalty-vector regularization. Journal of Advanced Computational Intelligence and Intelligent Informatic 15:1 (2011) 76-82   Pdf bib
 
  J. Ning, P.E. Cheng. A comparison study of nonparametric imputation methods. Statistics and Computing 0 (2011) 1-13   Pdf bib
 
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  S. Zhang. Shell-neighbor method and its application in missing data imputation. Applied Intelligence 35:1 (2011) 123-133   Pdf bib
 
  X. Zhu, S. Zhang, Z. Jin, Z. Zhang, Z. Xu. Missing value estimation for mixed-attribute data sets. IEEE Transactions on Knowledge and Data Engineering 23:1 (2011) 110-121   Pdf bib
 
  B. Zhu, C. He, P. Liatsis. A robust missing value imputation method for noisy data. Applied Intelligence 0 (2011) 1-14   Pdf bib
 

2010

  W-K. Ching, L. Li, N.K. Tsing, C.W. Tai, T.W. Ng, A.S. Wong. A Weighted Local Least Squares Imputation method for missing value estimation in microarray gene expression data. International Journal of Data Mining and Bioinformatics 4:3 (2010) 331-347   Pdf bib
 
  Y. Ding, J.S. Simonoff. An Investigation of Missing Data Methods for Classification Trees Applied to Binary Response Data. Journal of Machine Learning Research 11 (2010) 131-170   Pdf bib
 
  M. Ghannad-Rezaie, H. Soltanian-Zadeh, H. Ying. Selection-fusion approach for classification of datasets with missing values. Pattern Recognition 43:6 (2010) 2340-2350   Pdf bib
 
  M. Ghannad-Rezaie, H. Soltanian-Zadeh, H. Ying, M. Dong. Selection-fusion approach for classification of data sets with missing values. Pattern Recognition 43 (2010) 2340-2350   Pdf bib
 
  I.A. Gheyas, L.S. Smith. A neural network-based framework for the reconstruction of incomplete data sets. Neurocomputing 73:16-18 (2010) 3039-3065   Pdf bib
 
  T.P. Hong, L.H. Tseng, B.C. Chien. Mining from incomplete quantitative data by fuzzy rough sets. Expert Systems With Applications 37:3 (2010) 2644-2653   Pdf bib
 
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  P. Merlin, A. Sorjamaa, B. Maillet, A. Lendasse. X-SOM and L-SOM: A double classification approach for missing value imputation. Neurocomputing 0 (2010) 0-0   Pdf bib
 
  B. Twala, M. Cartwright. Ensemble missing data techniques for software effort prediction. Intelligent Data Analysis 14:3 (2010) 299-331   Pdf bib
 

2009

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  B. Twala. An empirical comparison of techniques for handling incomplete data using decision trees. Applied Artificial Intelligence 23 (2009) 373-405   Pdf bib
 

2008

  G. Corani, M. Zaffalon. Learning Reliable Classifiers From Small or Incomplete Data Sets: The Naive Credal Classifier 2. Journal of Machine Learning Research 9 (2008) 581-621   Pdf bib
 
  A. Farhangfar, L. Kurgan, J. Dy. Impact of imputation of missing values on classification error for discrete data. Pattern Recognition 41 (2008) 3692-3705   Pdf bib
 
  Q. Song, M. Shepperd, X. Chen, J. Liu. Can k-NN imputation improve the performance of C4.5 with small software project data sets? A comparative evaluation. Journal of Systems and Software 81:12 (2008) 2361-2370   Pdf bib
 

2007

  J. Banasik, J. Crook. Reject inference, augmentation, and sample selection. European Journal of Operational Research 183:3 (2007) 1582-1594   Pdf bib
 
  L.P. Bras, J.C. Menezes. Improving cluster-based missing value estimation of DNA microarray data. Biomolecular Engineering 24:2 (2007) 273-282   Pdf bib
 
  F.A. Dah. Convergence of random k-nearest-neighbour imputation. Computational Statistics & Data Analysis 51:12 (2007) 5913-5917   Pdf bib
 
  M. Di Zio, U. Guarnera, O. Luzi. Imputation through finite Gaussian mixture models,. Computational Statistics and Data Analysis 51:11 (2007) 5305-5316   Pdf bib
 
  A. Farhangfar, L.A. Kurgan, W. Pedrycz. A novel framework for imputation of missing values in databases. IEEE Transactions on Systems, Man, and Cybernetics 37:5 (2007) 692-709   Pdf bib
 
  J.W. Graham, A.E. Olchowski, T.D. Gilreath. How many imputations are really needed? Some practical clarifications of multiple imputation theory. Prevention Science 8:3 (2007) 206-213   Pdf bib
 
  E.R. Hruschka Jr., E.R. Hruschka, N.F.F. Ebecken. Bayesian networks for imputation in classification problems. Journal of Intelligent Information Systems 29:3 (2007) 231-252   Pdf bib
 
  K. Metaxoglou, A. Smith. Maximum likelihood estimation of VARMA models using a state-space em algorithm. Journal of Time Series Analysis 28:5 (2007) 666-685   Pdf bib
 
  M. Mojirsheibani. Nonparametric curve estimation with missing data: A general empirical process approach. ournal of Statistical Planning and Inference 137:9 (2007) 2733-2758   Pdf bib
 
  J.D. Parker, N. Schenker. Multiple imputation for national public-use datasets and its possible application for gestational age in United States Natality files. Paediatric and Perinatal Epidemiology 21:2 (2007) 97-105   Pdf bib
 
  H. Peng, S. Zhu. Handling of incomplete data sets using ICA and SOM in data mining. Neural Computing and Applications 16:2 (2007) 167-172   Pdf bib
 
  Y. Qin, S. Zhang, X. Zhu, J. Zhang, C. Zhang. Semi-parametric optimization for missing data imputation. Applied Intelligence 27:1 (2007) 79-88   Pdf bib
 
  M. Saar-Tsechansky, F. Provost. Handling missing values when applying classification models. Journal of Machine Learning Research 8 (2007) 1625-1657   Pdf bib
 
  T.H. Scheike, Y. Sun. Maximum likelihood estimation for tied survival data under Cox regression model via EM-algorithm. Lifetime Data Analysis 13:3 (2007) 399-420   Pdf bib
 
  Q. Song, M. Shepperd. A new imputation method for small software project data sets. Journal of Systems and Software 80:1 (2007) 51-62   Pdf bib
 
  D. Williams, X. Liao, Y. Xue, L. Carin, B. Krishnapuram. On Classification with Incomplete Data. IEEE Transactions on Pattern Analysis and Machine Intelligence 29:3 (2007) 427-436   Pdf bib
 
  D.S.V. Wong, F.K. Wong, G.R. Wood. A multi-stage approach to clustering and imputation of gene expression profiles. Bioinformatics 23:8 (2007) 998-1005   Pdf bib
 
  D. Yoon, E.K. Lee, T. Park. Robust imputation method for missing values in microarray data. BMC bioinformatics 8:2 (2007) 1-7   Pdf bib
 

2006

  X.B.C. Chai, R.A.D. Pan. Test-cost sensitive classification on data with missing values. IEEE Transactions on Knowledge and Data Engineering 18:5 (2006) 626-637   Pdf bib
 
  I.A. Fortes, L.B. Mora-Lopez, R.B. Morales, F.B. Triguero. Inductive learning models with missing values. Mathematical and Computer Modelling 44:9-10 (2006) 790-806   Pdf bib
 
  R.S. Lokupitiya, E.B. Lokupitiya, K.B. Paustian. Comparison of missing value imputation methods for crop yield data. Environmetrics 17:4 (2006) 339-349   Pdf bib
 
  M.K. Markey, G.D. Tourassi, M. Margolis, D.M. DeLong. Impact of missing data in evaluating artificial neural networks trained on complete data. Computers in Biology and Medicine 36:5 (2006) 516-525   Pdf bib
 
  A. Vellido. Missing data imputation through GTM as a mixture of t-distributions. Neural Networks 19:10 (2006) 1624-1635   Pdf bib
 
  X. Wang, Z. Jiang, H. Feng. Missing value estimation for DNA microarray gene expression data by Support Vector Regression imputation and orthogonal coding scheme. BMC Bioinformatics 7:32 (2006) 1-10   Pdf bib
 

2005

  M. Abdella, T. Marwala. The use of genetic algorithms and neural networks to approximate missing data in database. Computing and Informatics 24:6 (2005) 577-589   Pdf bib
 
  S.M. Chen, H.R. Hsiao. A new method to estimate null values in relational database systems based on automatic clustering techniques. Information Sciences 169:1 (2005) 47-69   Pdf bib
 
  S.M. Chen, S.W. Lee. Estimating null values in relational database systems based on genetic algorithms. Cybernetics and Systems 36:1 (2005) 85-106   Pdf bib
 
  H.A. Kim, G.H.B. Golub, H.A. Park. Missing value estimation for DNA microarray gene expression data: Local least squares imputation. Bioinformatics 21:2 (2005) 187-198   Pdf bib
 
  S. Konias, I.A. Chouvarda, I.B. Vlahavas, N.A. Maglaveras. A novel approach for incremental uncertainty rule generation from databases with missing values handling: Application to dynamic medical databases. Medical Informatics and the Internet in Medicine 30:3 (2005) 211-225   Pdf bib
 
  K.A. Pelckmans, J.B. De Brabanter, J.A.K.A. Suykens, B.A. De Moor. Handling missing values in support vector machine classifiers. Neural Networks 18:5-6 (2005) 684-692   Pdf bib
 
  I.A. Scheel, M.B. Aldrin, I.K.A. Glad, R.A. Sorum, H.C. Lyng, A.B. Frigessi. The influence of missing value imputation on detection of differentially expressed genes from microarray data. Bioinformatics 21:23 (2005) 4272-4279   Pdf bib
 
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2004

  O.T. Abdala, M.A. Saeed. Estimation of missing values in clinical laboratory measurements of ICU patients using a weighted K-nearest neighbors algorithm. Computers in Cardiology 31 (2004) 693-696   Pdf bib
 
  F.A. Barzi, M.A. Woodward. Imputations of missing values in practice: Results from imputations of serum cholesterol in 28 cohort studies. American Journal of Epidemiology 160:1 (2004) 34-45   Pdf bib
 
  T.H. Bo, B. Dysvik, I. Jonassen. LSimpute: accurate estimation of missing values in microarray data with least squares methods.. Nucleic acids research 32:3 (2004) 1-8   Pdf bib
 
  A. Figueroa, J.B. Borneman, T.A. Jiang. Clustering binary fingerprint vectors with missing values for DNA array data analysis. Journal of Computational Biology 11:5 (2004) 887-901   Pdf bib
 
  P.A. Gourraud, E.B. Génin, A.A. Cambon-Thomsen. Handling missing values in population data: Consequences for maximum likelihood estimation of haplotype frequencies. European Journal of Human Genetics 12:10 (2004) 805-812   Pdf bib
 
  K. Honda, H. Ichihashi. Linear fuzzy clustering techniques with missing values and their application to local principal component analysis. IEEE Transactions on Fuzzy Systems 12:2 (2004) 183-193   Pdf bib
 
  R.A. Little, H.A. An. Robust likelihood-based analysis of multivariate data with missing values. Statistica Sinica 14:3 (2004) 949-968   Pdf bib
 

2003

  G.E.A.P.A. Batista, M.C. Monard. An analysis of four missing data treatment methods for supervised learning. Applied Artificial Intelligence 17 (2003) 519-533   Pdf bib
 
  S.M. Chen, C.M. Huang. Generating weighted fuzzy rules from relational database systems for estimating null values using genetic algorithms. IEEE Transactions on Fuzzy Systems 11:4 (2003) 495-506   Pdf bib
 
  S.M. Chen, S.W. Lee. A new method to generate fuzzy rules from relational database systems for estimating null values. Cybernetics and Systems 34:1 (2003) 33-57   Pdf bib
 
  S.A. Oba, M.A. Sato, I.C. Takemasa, M.C. Monden, K.I. Matsubara, S.A. Ishii. A Bayesian missing value estimation method for gene expression profile data. Bioinformatics 19:16 (2003) 2088-2096   Pdf bib
 
  S.M. Tseng, K.H. Wang, C.I. Lee. A pre-processing method to deal with missing values by integrating clustering and regression techniques. Applied Artificial Intelligence 17:5-6 (2003) 535-544   Pdf bib
 
  X.A. Zhou, X.B. Wang, E.R. Dougherty. Missing-value estimation using linear and non-linear regression with Bayesian gene selection. Bioinformatics 19:17 (2003) 2302-2307   Pdf bib
 

2002

  B. Gabrys. Neuro-fuzzy approach to processing inputs with missing values in pattern recognition problems. International Journal of Approximate Reasoning 30:3 (2002) 149-179   Pdf bib
 
  X. Huang, Q. Zhu. A pseudo-nearest-neighbor approach for missing data. Pattern Recognition Letters 23:13 (2002) 1613-1622   Pdf bib
 
  J.L. Schafer, J.W. Graham. Missing data: our view of the state of the art. Psychol Methods 7:2 (2002) 147-177   Pdf bib
 
  J.L. Schafer, R.M. Yucel. Computational strategies for multivariate linear mixed-effects models with missing values . Journal of Computational and Graphical Statistics 11:2 (2002) 437-457   Pdf bib
 

2001

  C.M. Ennett, M. Frize, C.R. Walker. Influence of missing values on artificial neural network performance. Medinfo 10 (2001) 449-453   Pdf bib
 
  T. Schneider. Analysis of incomplete climate data: Estimation of Mean Values and covariance matrices and imputation of Missing values. Journal of Climate 14 (2001) 853-871   Pdf bib
 
  O. Troyanskaya, M. Cantor, G. Sherlock, P. Brown, T. Hastie, R. Tibshirani, D. Botstein, R.B. Altman. Missing value estimation methods for DNA microarrays. Bioinformatics 17:6 (2001) 520-525   Pdf bib
 

1999

  M. Kryszkiewicz. Rules in incomplete information systems. Information Sciences 113 (1999) 271-292   Pdf bib
 

1998

  M.R. Berthold, K.P. Huber. Missing Values and Learning of Fuzzy Rules. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 6:2 (1998) 171-178   Pdf bib
 

1997

  S.M. Chen, M.S. Yeh. Generating fuzzy rules from relational database systems for estimating null values. Cybernetics and Systems 28:8 (1997) 695-723   Pdf bib
 




Main  Contributions to Books Chapters

Jump to Year:   2004



2004

  E. Acuna, C. Rodriguez. The treatment of missing values and its effect in the classifier accuracy. In: W. Gaul, D. Banks, L. House, F.R. McMorris, P. Arabie (Eds.) Classification, Clustering and Data Mining Applications, Springer-Verlag Berlin-Heidelberg, 2004, 639-648   Pdf bib
 




Main  Conference Contributions

Jump to Year:
        2010  2008  2007  2006  2005  2004  2003  2001  1999  1998
      



2010

  A. Bolotin. A new method of multiple imputation for completely (or almost completely) missing data. International Conference on Mathematical and Computational Methods in Science and Engineering. (2010) 34-45   Pdf bib
 
  F. Qin, J. Lee. Dynamic methods for missing value estimation for DNA sequences. 2010 International Conference on Computational and Information Sciences (ICCIS 2010). (2010) 442-445   Pdf bib
 
  S. Zhang, X. Wu, M. Zhu. Efficient missing data imputation for supervised learning. 9th IEEE International Conference on Cognitive Informatics (ICCI 2010). (2010) 672-679   Pdf bib
 

2008

  E.T. Matsubara,, R.C. Prati, G.E.A.P.A. Batista, M.C. Monard. Missing Value Imputation Using a Semi-supervised Rank Aggregation Approach. 19th Brazilian Symposium on Artificial Intelligence (SBIA 2008). Lecture Notes in Computer Science 5249, Springer 2008, Salvador Bahia (Brazil, 2008) 217-226   Pdf bib
 

2007

  H. Ichihashi, K. Honda, Y. Kuramoto, F. Matsuura. Fuzzy c-Means Classifier for Relational Data. 2007 IEEE Symposium on (CIDM 2007). (2007) 328-334   Pdf bib
 
  B.M. Nogueira, T.R.A. Santos, L.E. Zarate. Comparison of Classifiers Efficiency on Missing Values Recovering: Application in a Marketing Database with Massive Missing Data. Computational Intelligence and Data Mining (CIDM2007). (2007) 66-72   Pdf bib
 
  L.E. Zarate, B.M. Nogueira, T.R.A. Santos, M.A.J. Song. Techniques for missing value recovering in imbalanced databases: Application in a marketing database with massive missing data. IEEE International Conference on Systems, Man and Cybernetics. (2007) 2658-2664   Pdf bib
 

2006

  Z. Xia, Y. Dong, G. Xing. Support vector machines for collaborative filtering. Annual Southeast Conference. (2006) 169-174   Pdf bib
 
  K.A. Yang, J.A. Li, C.A. Wang. Missing values estimation in microarray data with partial least squares regression. International Workshop on Bioinformatics Research and Applications (IWBRA2006). Lecture Notes in Computer Science 3992, 2006 (2006) 662-669   Pdf bib
 

2005

  H.A.B. Feng, G.C. Chen, C.D. Yin, B.B. Yang, Y.E. Chen. A SVM regression based approach to filling in missing values. Knowledge-Based Intelligent Information and Engineering Systems (KES05). Lecture Notes in Computer Science 3683, 2005 (2005) 581-587   Pdf bib
 
  T.R. Gabriel, M.R. Berthold. Missing values in fuzzy rule induction. IEEE International Conference on Systems, Man and Cybernetics. (2005) 1473-1476   Pdf bib
 
  J.W. Grzymala-Busse, L.K. Goodwin, J. Witold, J. Grzymala-Busse, X. Zheng. Handling Missing Attribute Values in Preterm Birth Data Sets. Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. Lecture Notes in Computer Science 3642, 2005 (2005) 342-351   Pdf bib
 
  E.R. Hruschka, E.R. Hruschka Jr., N.F.F. Ebecken. Missing values imputation for a clustering genetic algorithm. Advances in Natural Computation - First International Conference (ICNC2006). Lecture Notes in Computer Science 3612, 2005 (2005) 245-254   Pdf bib
 
  M.S.B. Sehgal, I. Gondal, L. Dooley. Collateral missing value estimation: Robust missing value estimation for consequent microarray data processing. AI 2005: Advances in Artificial Intelligence. Lecture Notes in Computer Science 3809, 2005 (2005) 2417-2423   Pdf bib
 
  M.S.B. Sehgal, I. Gondal, L. Dooley. K-ranked covariance based missing values estimation for microarray data classification. 4th International Conference on Hybrid Intelligent Systems (HIS04). (2005) 274-279   Pdf bib
 

2004

  S. Alonso, F. Chiclana, F. Herrera, E. Herrera-Viedma. A Learning Procedure to Estimate Missing Values in Fuzzy Preference Relations Based on Additive Consistency. Modeling Decisions for Artificial Intelligence: First International Conference (MDAI2004). Lecture Notes in Computer Science 3131, 2004 (2004) 227-238   Pdf bib
 
  J. Deogun, W. Spaulding, D. Li. Towards Missing Data Imputation: A Study of Fuzzy K-means Clustering Method. Rough Sets and Current Trends in Computing. Lecture Notes in Computer Science 3066, 2004 (2004) 573-579   Pdf bib
 
  A. Farhangfar, L. Kurgan, W. Pedrycz. Experimental analysis of methods for imputation of missing values in databases. SPIE - The International Society for Optical Engineering. (2004) 172-182   Pdf bib
 
  W.J. Grzymala-Busse. Data with missing attribute values: Generalization of idiscernibility relation and rule induction. Transactions on Rough Sets. Lecture Notes in Computer Science 3100, 2004 (2004) 78-95   Pdf bib
 
  R. Latkowski, M. Mikolajczyk. Data Decomposition and Decision Rule Joining for Classification of Data with Missing Values. Rough Sets and Current Trends in Computing. Lecture Notes in Computer Science 3066, 2004 (2004) 254-263   Pdf bib
 

2003

  W.J. Grzymala-Busse. Rough set strategies to data with missing attribute values. the Workshop on Foundations and New Directions in Data Mining, associated with the third IEEE International Conference on Data Mining. (2003) 56-63   Pdf bib
 
  E.R. Hruschka, N.F.F. Ebecken. Evaluating a Nearest-Neighbor Method to Substitute Continuous Missing Values. 16th Australian Conference on Artificial Intelligence. Lecture Notes in Computer Science 2903, 2003 (2003) 723-734   Pdf bib
 

2001

  J.W. Grzymala-Busse, M. Hu. A Comparison of Several Approaches to Missing Attribute Values in Data Mining. Rough Sets and Current Trends in Computing : Second International Conference (RSCTC 2000). Lecture Notes in Computer Science 2005, 2001 (2001) 378-385   Pdf bib
 
  M. Sarkar, T.Y. Leong. Fuzzy K-means clustering with missing values. Annual Symposium. AMIA Symposium. (2001) 588-592   Pdf bib
 

1999

  Jerzy W. Grzymala-Busse, Witold J. Grzymala-Busse, Linda K. Goodwin. A Closest Fit Approach to Missing Attribute Values in Preterm Birth Data. New Directions in Rough Sets, Data Mining, and Granular-Soft Computing. Lecture Notes in Computer Science 1711, 1999 (1999) 405-413   Pdf bib
 

1998

  M. Kryszkiewicz. Rough set strategies to data with missing attribute values. Second Annual Joint Conference on Information Sciences. (1998) 194-197   Pdf bib
 




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