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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 1/2018

29.03.2015 | Original Article

Information-decomposition-model-based missing value estimation for not missing at random dataset

verfasst von: Shigang Liu, Honghua Dai, Min Gan

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 1/2018

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Abstract

Missing data estimation is an important strategy for improving learning performance in learning from incomplete data, especially, when there are non discardable records with missing values. However, most of the existing algorithms are focused on missing at random (MAR) or missing completely at random (MCAR), and less attention has been paid to data not missing at random (NMAR). In this paper, an information decomposition imputation (IDIM) algorithm using fuzzy membership function is proposed for addressing the missing value problem under NMAR. Firstly, the proposed IDIM algorithm is presented with detailed examples. Then, the proposed approach is evaluated with extensive experiments compared with some typical algorithms. The experimental results demonstrate that the proposed algorithm has higher accuracy than the exiting imputation approaches in terms of normal root mean square error (NRMSE) and TP+TN evaluation under different missing strategies.
Vorheriger Artikel Sentimental feature selection for sentiment analysis of Chinese online reviews
Nächster Artikel A novel hybrid model using teaching–learning-based optimization and a support vector machine for commodity futures index forecasting

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Metadaten
Titel
Information-decomposition-model-based missing value estimation for not missing at random dataset
verfasst von
Shigang Liu
Honghua Dai
Min Gan
Publikationsdatum
29.03.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 1/2018
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-015-0354-5

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