NIIA: Nonparametric iterative imputation algorithm

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

Volumn 5351 LNAI, Issue , 2008, Pages 544-555

  Zhang, Shichao ^a,b,c   Jin, Zhi ^d   Zhu, Xiaofeng ^a  

^a GUANGXI NORMAL UNIVERSITY   (China)

^b UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^c NANJING UNIVERSITY   (China)

^d PEKING UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; AUTOCORRELATION; BIONICS; CLUSTERING ALGORITHMS;

DATA SETS; EFFICIENT ALGORITHMS; EXISTING METHODS; IMPUTATION ALGORITHMS; MISSING DATA IMPUTATIONS; MISSING VALUE IMPUTATIONS; MISSING VALUES; NEAREST NEIGHBORS; NONPARAMETRIC; TARGET VALUES;

ITERATIVE METHODS;

EID: 58349097997     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-89197-0_50     Document Type: Conference Paper

References (8)

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4. Pearson, P.K.: Mining imperfect data: dealing with contamination and incomplete records. SIAM, Philadelphia (2005)
5. Wang, Q., Rao, J.N.K.: Empirical likelihood-based inference under imputation for missing response data. Ann. Statist. 30, 896-924 (2002)
6. Zhang, C.Q., et al.: Efficient Imputation Method for Missing Values. In: Zhou, Z.-H., Li, H., Yang, Q. (eds.) PAKDD 2007. LNCS (LNAI), vol. 4426, pp. 1080-1087. Springer, Heidelberg (2007)
7. Qin, Y.S., et al.: Optimized parameters for missing data imputation. In: Yang, Q., Webb, G. (eds.) PRICAI 2006. LNCS (LNAI), vol.4099, pp. 1010-1016. Springer, Heidelberg (2006)
8. Qin, Y.S., et al.: POP Algorithm: Kernel-Based Imputation to Treat Missing Values in Knowledge Discovery from Databases. Expert Systems with Applications (accepted, 2008), http://dx.doi.org/10.1016/j.eswa.2 008.01.059