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Impute vs. Ignore: Missing values for prediction


Zhang, Q and Rahman, A and D'Este, CE, Impute vs. Ignore: Missing values for prediction, Proceedings of the 2013 International Joint Conference on Neural Networks, 4-9 August 2013, Dallas, Texas, United States, pp. 1-8. ISBN 978-1-4673-6129-3 (2013) [Non Refereed Conference Paper]

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DOI: doi:10.1109/IJCNN.2013.6707014


Sensor faults or communication errors can cause certain sensor readings to become unavailable for prediction purposes. In this paper we evaluate the performance of imputation techniques and techniques that ignore the missing values, in scenarios: (i) when values are missing only during prediction phase, and (ii) when values are missing during both the induction and prediction phase. We also investigated the influence of different scales of missingness on the performance of these treatments. The results can be used as a guideline to facilitate the choice of different missing value treatments under different circumstances.

Item Details

Item Type:Non Refereed Conference Paper
Keywords:sensor faults, impute, ignore, prediction, missing value,
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Knowledge representation and reasoning
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the environmental sciences
UTAS Author:D'Este, CE (Dr Claire D'Este)
ID Code:116719
Year Published:2013
Deposited By:Information and Communication Technology
Deposited On:2017-05-17
Last Modified:2017-05-17

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