Journal Contributions: Published

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        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			bib
	J. Ning, P.E. Cheng. A comparison study of nonparametric imputation methods. Statistics and Computing 0 (2011) 1-13			bib
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	S. Zhang. Shell-neighbor method and its application in missing data imputation. Applied Intelligence 35:1 (2011) 123-133			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			bib
	B. Zhu, C. He, P. Liatsis. A robust missing value imputation method for noisy data. Applied Intelligence 0 (2011) 1-14			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			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			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			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			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			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			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			bib
	B. Twala, M. Cartwright. Ensemble missing data techniques for software effort prediction. Intelligent Data Analysis 14:3 (2010) 299-331			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			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			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			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			bib
2007
	J. Banasik, J. Crook. Reject inference, augmentation, and sample selection. European Journal of Operational Research 183:3 (2007) 1582-1594			bib
	L.P. Bras, J.C. Menezes. Improving cluster-based missing value estimation of DNA microarray data. Biomolecular Engineering 24:2 (2007) 273-282			bib
	F.A. Dah. Convergence of random k-nearest-neighbour imputation. Computational Statistics & Data Analysis 51:12 (2007) 5913-5917			bib
	M. Di Zio, U. Guarnera, O. Luzi. Imputation through finite Gaussian mixture models,. Computational Statistics and Data Analysis 51:11 (2007) 5305-5316			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			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			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			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			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			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			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			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			bib
	M. Saar-Tsechansky, F. Provost. Handling missing values when applying classification models. Journal of Machine Learning Research 8 (2007) 1625-1657			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			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			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			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			bib
	D. Yoon, E.K. Lee, T. Park. Robust imputation method for missing values in microarray data. BMC bioinformatics 8:2 (2007) 1-7			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			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			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			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			bib
	A. Vellido. Missing data imputation through GTM as a mixture of t-distributions. Neural Networks 19:10 (2006) 1624-1635			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			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			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			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			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			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			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			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			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			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			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			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			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			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			bib
	R.A. Little, H.A. An. Robust likelihood-based analysis of multivariate data with missing values. Statistica Sinica 14:3 (2004) 949-968			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			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			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			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			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			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			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			bib
	X. Huang, Q. Zhu. A pseudo-nearest-neighbor approach for missing data. Pattern Recognition Letters 23:13 (2002) 1613-1622			bib
	J.L. Schafer, J.W. Graham. Missing data: our view of the state of the art. Psychol Methods 7:2 (2002) 147-177			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			bib
2001
	C.M. Ennett, M. Frize, C.R. Walker. Influence of missing values on artificial neural network performance. Medinfo 10 (2001) 449-453			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			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			bib
1999
	M. Kryszkiewicz. Rules in incomplete information systems. Information Sciences 113 (1999) 271-292			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			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			bib

 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			bib

 Conference Contributions

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        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			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			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			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			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			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			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			bib
2006
	Z. Xia, Y. Dong, G. Xing. Support vector machines for collaborative filtering. Annual Southeast Conference. (2006) 169-174			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			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			bib
	T.R. Gabriel, M.R. Berthold. Missing values in fuzzy rule induction. IEEE International Conference on Systems, Man and Cybernetics. (2005) 1473-1476			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			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			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			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			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			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			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			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			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			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			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			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			bib
	M. Sarkar, T.Y. Leong. Fuzzy K-means clustering with missing values. Annual Symposium. AMIA Symposium. (2001) 588-592			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			bib
1998
	M. Kryszkiewicz. Rough set strategies to data with missing attribute values. Second Annual Joint Conference on Information Sciences. (1998) 194-197			bib

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