Mining Imperfect Data: Dealing with Contamination and Incomplete Records
Mining Imperfect Data: Dealing with Contamination and Incomplete Records
Semi-parametric optimization for missing data imputation
Applied Intelligence
POP algorithm: Kernel-based imputation to treat missing values in knowledge discovery from databases
Expert Systems with Applications: An International Journal
GBKII: an imputation method for missing values
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Shell-neighbor method and its application in missing data imputation
Applied Intelligence
Combining kNN Imputation and Bootstrap Calibrated: Empirical Likelihood for Incomplete Data Analysis
International Journal of Data Warehousing and Mining
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In this paper, the author designs an efficient method for imputing iteratively missing target values with semi-parametric kernel regression imputation, known as the semi-parametric iterative imputation algorithm SIIA. While there is little prior knowledge on the datasets, the proposed iterative imputation method, which impute each missing value several times until the algorithms converges in each model, utilize a substantially useful amount of information. Additionally, this information includes occurrences involving missing values as well as capturing the real dataset distribution easier than the parametric or nonparametric imputation techniques. Experimental results show that the author's imputation methods outperform the existing methods in terms of imputation accuracy, in particular in the situation with high missing ratio.