Palczewska, A, Palczewski, J orcid.org/0000-0003-0235-8746, Aivaliotis, G et al. (1 more author) (2017) RobustSPAM for Inference from Noisy Longitudinal Data and Preservation of Privacy. In: IEEE ICMLA 2017 Conference proceedings. IEEE 16TH International Conference on Machine Learning and Applications - ICMLA 2017, 18-21 Dec 2017, Cancun, Mexico. Institute of Electrical and Electronics Engineers , pp. 344-351. ISBN 978-1-5386-1417-4
Abstract
The availability of complex temporal datasets in social, health and consumer contexts has driven the development of pattern mining techniques that enable the use of classical machine learning tools for model building. In this work we introduce a robust temporal pattern mining framework for finding predictive patterns in complex timestamped multivariate and noisy data. We design an algorithm RobustSPAM that enables mining of temporal patterns from data with noisy timestamps. We apply our algorithm to social care data from a local government body and investigate how the efficiency and accuracy of the method depends on the level of noise. We further explore the trade-off between the loss of predictivity due to perturbation of timestamps and the risk of person re-identification.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 IEEE. This is an author produced version of a paper published in IEEE ICMLA 2017 Conference Proceedings. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Uploaded in accordance with the publisher’s self-archiving policy. |
Keywords: | robust, temporal pattern, noisy data, privacy |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 06 Oct 2017 08:53 |
Last Modified: | 27 Mar 2018 10:39 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Identification Number: | 10.1109/ICMLA.2017.0-137 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:122134 |