GBKII: An imputation method for missing values

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4426 LNAI, Issue , 2007, Pages 1080-1087

  Zhang, Chengqi ^a,b   Zhu, Xiaofeng ^c   Zhang, Jilian ^c   Qin, Yongsong ^c   Zhang, Shichao ^c  

^a UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^b CITY UNIVERSITY OF HONG KONG   (Hong Kong)

^c GUANGXI NORMAL UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; DATA STRUCTURES; LEARNING SYSTEMS; REGRESSION ANALYSIS;

MEAN-SUBSTITUTION METHODS; MISSING DATA IMPUTATION;

DATA REDUCTION;

EID: 38149046554     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-71701-0_122     Document Type: Conference Paper

References (8)

1. Blake, C. and Merz, C. UCI Repository of machine learning databases. 1998.
2. Caruana, R. A Non-parametric EM-style algorithm for Imputing Missing Value. Artificial Intelligence and Statistics, January 2001.
3. Rubin, D.B., Multiple Imputation for Nonresponse in Surveys, Wiley: New York, 1987.
4. Dempster, A.P., Laird, N.M. and Rubin, D.B., Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, series B, Vol. 39, pp. 1-38, 1977.
5. Zhang, S.C., Qin, Y.S., Zhu, X.F., Zhang, J.L., and Zhang, C.Q. Optimized Parameters for Missing Data Imputation. PRICAI06, 2006: 1010-1016.
6. Huang, C.C. and Lee, H.M. An instance-based learning approach based on grey relational structure. In: Proc. of the UK Workshop on Computational Intelligence (UKCI-02), Birmingham, Sept. 2002.
7. Lakshminarayan, K. et al., Imputation of missing data in industrial databases, Applied Intelligence, Vol. 11, pp. 259-275, 1999.
8. Brown, M.L. Data mining and the impact of missing data. Industrial Management & Data Systems, Vol. 103/8, pp. 611-621, 2003.