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

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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.

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Authors and Affiliations

  1. School of Information Technology, Deakin University, Melbourne, VIC, 3125, Australia

    Shigang Liu & Honghua Dai

  2. Planning and Institutional Performance Unit, Planning and Governance, La Trobe University, Melbourne, VIC, 3083, Australia

    Min Gan

Authors
  1. Shigang Liu
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  2. Honghua Dai
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  3. Min Gan
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Correspondence to Shigang Liu.

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Liu, S., Dai, H. & Gan, M. Information-decomposition-model-based missing value estimation for not missing at random dataset. Int. J. Mach. Learn. & Cyber. 9, 85–95 (2018). https://doi.org/10.1007/s13042-015-0354-5

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  • DOI: https://doi.org/10.1007/s13042-015-0354-5

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