Impact of imputation of missing values on classification error for discrete data
Pattern Recognition
Aprimorando processos de imputação multivariada de dados com workflows
SBBD '08 Proceedings of the 23rd Brazilian symposium on Databases
Missing Data Analysis: A Kernel-Based Multi-Imputation Approach
Transactions on Computational Science III
Imputation of missing sensor data values using in-exact replicas
International Journal of Intelligent Systems Technologies and Applications
Shell-neighbor method and its application in missing data imputation
Applied Intelligence
Complementing data in the ETL process
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
Expert Systems with Applications: An International Journal
A robust missing value imputation method for noisy data
Applied Intelligence
A classifier ensemble approach for the missing feature problem
Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
Information enhancement for data mining
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Information Sciences: an International Journal
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Many of the industrial and research databases are plagued by the problem of missing values. Some evident examples include databases associated with instrument maintenance, medical applications, and surveys. One of the common ways to cope with missing values is to complete their imputation (filling in). Given the rapid growth of sizes of databases, it becomes imperative to come up with a new imputation methodology along with efficient algorithms. The main objective of this paper is to develop a unified framework supporting a host of imputation methods. In the development of this framework, we require that its usage should (on average) lead to the significant improvement of accuracy of imputation while maintaining the same asymptotic computational complexity of the individual methods. Our intent is to provide a comprehensive review of the representative imputation techniques. It is noticeable that the use of the framework in the case of a low-quality single-imputation method has resulted in the imputation accuracy that is comparable to the one achieved when dealing with some other advanced imputation techniques. We also demonstrate, both theoretically and experimentally, that the application of the proposed framework leads to a linear computational complexity and, therefore, does not affect the asymptotic complexity of the associated imputation method.