期刊
KNOWLEDGE-BASED SYSTEMS
卷 53, 期 -, 页码 51-65出版社
ELSEVIER
DOI: 10.1016/j.knosys.2013.08.023
关键词
Data pre-processing; Data cleansing; Missing value imputation; Decision tree algorithm; Decision forest algorithm; EM algorithm
We present two novel techniques for the imputation of both categorical and numerical missing values. The techniques use decision trees and forests to identify horizontal segments of a data set where the records belonging to a segment have higher similarity and attribute correlations. Using the similarity and correlations, missing values are then imputed. To achieve a higher quality of imputation some segments are merged together using a novel approach. We use nine publicly available data sets to experimentally compare our techniques with a few existing ones in terms of four commonly used evaluation criteria. The experimental results indicate a clear superiority of our techniques based on statistical analyses such as confidence interval. (C) 2013 Elsevier B.V. All rights reserved.
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