期刊
APPLIED SOFT COMPUTING
卷 29, 期 -, 页码 65-74出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.asoc.2014.09.052
关键词
Hot-deck model; Multiple imputation; Mean/mode model; Multilayer perceptron; Regression model
The knowledge discovery process is supported by data files information gathered from collected data sets, which often contain errors in the form of missing values. Data imputation is the activity aimed at estimating values for missing data items. This study focuses on the development of automated data imputation models, based on artificial neural networks for monotone patterns of missing values. The present work proposes a single imputation approach relying on a multilayer perceptron whose training is conducted with different learning rules, and a multiple imputation approach based on the combination of multilayer perceptron and k-nearest neighbours. Eighteen real and simulated databases were exposed to a perturbation experiment with random generation of monotone missing data pattern. An empirical test was accomplished on these data sets, including both approaches (single and multiple imputations), and three classical single imputation procedures - mean/mode imputation, regression and hot-deck - were also considered. Therefore, the experiments involved five imputation methods. The results, considering different performance measures, demonstrated that, in comparison with traditional tools, both proposals improve the automation level and data quality offering a satisfactory performance. (C) 2015 Elsevier B.V. All rights reserved.
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