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Impute vs. Ignore: Missing values for prediction

Citation

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

Abstract

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 and Image Processing
Research Field:Expert Systems
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in Technology
Author:D'Este, CE (Dr Claire D'Este)
ID Code:116719
Year Published:2013
Deposited By:Computing and Information Systems
Deposited On:2017-05-17
Last Modified:2017-05-17
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