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      Driftage: a multi-agent system framework for concept drift detection

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          Abstract

          Background

          The amount of data and behavior changes in society happens at a swift pace in this interconnected world. Consequently, machine learning algorithms lose accuracy because they do not know these new patterns. This change in the data pattern is known as concept drift. There exist many approaches for dealing with these drifts. Usually, these methods are costly to implement because they require (i) knowledge of drift detection algorithms, (ii) software engineering strategies, and (iii) continuous maintenance concerning new drifts.

          Results

          This article proposes to create Driftage: a new framework using multi-agent systems to simplify the implementation of concept drift detectors considerably and divide concept drift detection responsibilities between agents, enhancing explainability of each part of drift detection. As a case study, we illustrate our strategy using a muscle activity monitor of electromyography. We show a reduction in the number of false-positive drifts detected, improving detection interpretability, and enabling concept drift detectors’ interactivity with other knowledge bases.

          Conclusion

          We conclude that using Driftage, arises a new paradigm to implement concept drift algorithms with multi-agent architecture that contributes to split drift detection responsability, algorithms interpretability and more dynamic algorithms adaptation.

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          Most cited references53

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          Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

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            A survey on concept drift adaptation

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              Characterizing concept drift

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                Author and article information

                Contributors
                Journal
                Gigascience
                Gigascience
                gigascience
                GigaScience
                Oxford University Press
                2047-217X
                01 June 2021
                June 2021
                01 June 2021
                : 10
                : 6
                : giab030
                Affiliations
                Informatics Department, Pontifical Catholic University of Rio de Janeiro (PUC-Rio) , Marques de São Vicente, 225, Gávea, Rio de Janeiro, RJ 22451-900, Brazil
                Informatics Department, Pontifical Catholic University of Rio de Janeiro (PUC-Rio) , Marques de São Vicente, 225, Gávea, Rio de Janeiro, RJ 22451-900, Brazil
                Informatics Department, Pontifical Catholic University of Rio de Janeiro (PUC-Rio) , Marques de São Vicente, 225, Gávea, Rio de Janeiro, RJ 22451-900, Brazil
                Informatics Department, Pontifical Catholic University of Rio de Janeiro (PUC-Rio) , Marques de São Vicente, 225, Gávea, Rio de Janeiro, RJ 22451-900, Brazil
                Author notes
                Correspondence address. Diogo Munaro Vieira, Informatics Department, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil. E-mail: dvieira@ 123456inf.puc-rio.br
                Correspondence address. Sérgio Lifschitz, Informatics Department, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil. E-mail: sergio@ 123456inf.puc-rio.br
                Author information
                https://orcid.org/0000-0002-8401-8843
                https://orcid.org/0000-0003-3073-3734
                Article
                giab030
                10.1093/gigascience/giab030
                8168350
                34061207
                d9aedab4-aff7-4a20-b624-cda4dd7575ec
                © The Author(s) 2021. Published by Oxford University Press GigaScience.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 24 September 2020
                : 07 March 2021
                : 30 March 2021
                Page count
                Pages: 10
                Funding
                Funded by: Conselho Nacional de Desenvolvimento Científico e Tecnológico, DOI 10.13039/501100003593;
                Funded by: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, DOI 10.13039/501100002322;
                Categories
                Research
                AcademicSubjects/SCI00960
                AcademicSubjects/SCI02254

                concept drift,data drift,anomaly detection,time series,multi-agent systems,data mining,machine learning interpretability,machine learning explainability

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