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Title: Dealing with under-reported variables: An information theoretic solution
Authors: Sechidis, Konstantinos
Sperrin, Matthew
Petherick, Emily S.
Lujan, Mikel
Brown, Gavin
Keywords: Under-reporting
Misclassification bias
Missing data
Mutual information
Issue Date: 2017
Publisher: Elsevier © The Authors
Citation: SECHIDIS, K. ... et al, 2017. Dealing with under-reported variables: An information theoretic solution. International Journal of Approximate Reasoning, 85, pp. 159-177.
Abstract: Under-reporting occurs in survey data when there is a reason for participants to give a false negative response to a question, e.g. maternal smoking in epidemiological studies. Failing to correct this misreporting introduces biases and it may lead to misinformed decision making. Our work provides methods of correcting for this bias, by reinterpreting it as a missing data problem, and particularly learning from positive and unlabelled data. Focusing on information theoretic approaches we have three key contributions: (1) we provide a method to perform valid independence tests with known power by incorporating prior knowledge over misreporting; (2) we derive corrections for point/interval estimates of the mutual information that capture both relevance and redundancy; and finally, (3) we derive different ways for ranking under-reported risk factors. Furthermore, we show how to use our results in real-world problems and machine learning tasks.
Description: This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/
Sponsor: This study was partly supported by the University of Manchester's Health eResearch Centre (HeRC) funded by the Medical Research Council (MRC) Grant MR/K006665/1. Konstantinos Sechidis, Mikel Luján and Gavin Brown were supported by the Engineering and Physical Sciences Research Council through the Centre for Doctoral Training grant [EP/I028099/1] and the Anyscale Apps project grant [EP/L000725/1].
Version: Published
DOI: 10.1016/j.ijar.2017.04.002
URI: https://dspace.lboro.ac.uk/2134/24901
Publisher Link: http://dx.doi.org/10.1016/j.ijar.2017.04.002
ISSN: 0888-613X
Appears in Collections:Published Articles (Sport, Exercise and Health Sciences)

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