An Ensemble Interpretable Machine Learning Scheme for Securing Data Quality at the Edge

Karanika, A., Oikonomou, P., Kolomvatsos, K. and Anagnostopoulos, C. (2020) An Ensemble Interpretable Machine Learning Scheme for Securing Data Quality at the Edge. In: CD-MAKE Cross Domain Conference for Machine Learning and Knowledge Extraction, All-Digital Conference, 25-28 Aug 2020, pp. 517-534. ISBN 9783030573201 (doi: 10.1007/978-3-030-57321-8_29)

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Data quality is a significant research subject for any application that requests for analytics to support decision making. It becomes very important when we focus on Internet of Things (IoT) where numerous devices can interact to exchange and process data. IoT devices are connected to Edge Computing (EC) nodes to report the collected data, thus, we have to secure data quality not only at the IoT infrastructure but also at the edge of the network. In this paper, we focus on the specific problem and propose the use of interpretable machine learning to deliver the features that are important to be based on for any data processing activity. Our aim is to secure data quality for those features, at least, that are detected as significant in the collected datasets. We have to notice that the selected features depict the highest correlation with the remaining ones in every dataset, thus, they can be adopted for dimensionality reduction. We focus on multiple methodologies for having interpretability in our learning models and adopt an ensemble scheme for the final decision. Our scheme is capable of timely retrieving the final result and efficiently selecting the appropriate features. We evaluate our model through extensive simulations and present numerical results. Our aim is to reveal its performance under various experimental scenarios that we create varying a set of parameters adopted in our mechanism.

Item Type:Conference Proceedings
Additional Information:CD-MAKE is a joint effort of IFIP TC 5, IFIP TC 12, IFIP WG 8.4, IFIP WG 8.9 and IFIP WG 12.9 and is held as an all-digital conference in conjunction with the 15th International Conference on Availability, Reliability and Security ARES 2020.
Glasgow Author(s) Enlighten ID:Anagnostopoulos, Dr Christos
Authors: Karanika, A., Oikonomou, P., Kolomvatsos, K., and Anagnostopoulos, C.
College/School:College of Science and Engineering > School of Computing Science
Published Online:18 August 2020
Copyright Holders:Copyright © IFIP International Federation for Information Processing 2020
First Published:First published in Lecture Notes in Computer Science book series (LNCS, volume 12279)
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
301654Intelligent Applications over Large Scale Data StreamsChristos AnagnostopoulosEuropean Commission (EC)745829Computing Science