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        Iterative Robust Semi-Supervised Missing Data Imputation

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        Author
        Fazakis, Nikos
        Kostopoulos, Georgios
        Kotsiantis, Sotiris
        Mporas, Iosif
        Attention
        2299/22755
        Abstract
        In many real-world applications scientists are often confronted with the problem of incomplete datasets due to several reasons. The direct analysis of datasets with missing values in attributes inevitably results in inaccurate learning models and erroneous results. Facing effectively the challenge of missing values is an essential step of the data mining process. Imputation is often employed to overcome the shortcomings incurred by missing data during the pre-process stage of data analysis. Therefore, a plethora of statistical and machine learning methods have been proposed and employed with a view to imputing the missing values in incomplete data with their potential or actual values. In this context, the main objective of this paper is to put forward an iterative stepwise imputation method based on the semi-supervised learning approach, called IRSSI. Semi-supervised methods have proved to be particularly effective for exploiting incomplete or partially labeled data with regard to the values of the target attribute. The proposed algorithm was experimentally evaluated on real-world benchmark datasets and artificially generated datasets using different high ratios of missing data. The experimental results demonstrate the efficiency of IRSSI algorithm compared to typical imputation methods.
        Publication date
        2020-05-12
        Published in
        IEEE Access
        Published version
        https://doi.org/10.1109/ACCESS.2020.2994033
        License
        http://creativecommons.org/licenses/by/4.0/
        Other links
        http://hdl.handle.net/2299/22755
        Relations
        School of Physics, Engineering & Computer Science
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