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A Novel Probabilistic Approach for Detecting Concept Drift in Streaming Data

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Deep Learning Theory and Applications (DeLTA 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1875))

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Abstract

Concept drift, which indicates data-distribution changes in streaming scenarios, can significantly reduce predictive performance. Existing concept drift detection methods often struggle with the trade-off between fast detection and low false alarm rates. This paper presents a novel concept drift detection algorithm, called SPNCD*, based on probabilistic methods, particularly Sum-Product Networks, that addresses this challenge by offering high detection accuracy and low mean lag time. Based on three benchmark datasets, the proposed method is evaluated against state-of-the-art algorithms, such as DDM, ADWIN, KSWIN, and HDDM_A. Our experiments demonstrate that SPNCD* outperforms the existing algorithms in terms of true positive rate, recall, precision, and mean lag time while improving the performance of the base classifier. The SPNCD* algorithm provides a reliable solution for detecting concept drift in real-time streaming data, enabling practitioners to maintain their machine learning models’ performance in dynamic environments.

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Authors and Affiliations

  1. Computer Science Department, University of Regina, 3737, Regina, Wascana Parkway, Canada

    Sirvan Parasteh & Samira Sadaoui

Authors
  1. Sirvan Parasteh
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  2. Samira Sadaoui
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Correspondence to Sirvan Parasteh .

Editor information

Editors and Affiliations

  1. Université de Tours, Tours, France

    Donatello Conte

  2. Instituto de Telecomunicações and University of Lisbon, Lisbon, Portugal

    Ana Fred

  3. Ford Motor Company, Commerce Township, MI, USA

    Oleg Gusikhin

  4. University of Naples Federico II, Naples, Italy

    Carlo Sansone

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Parasteh, S., Sadaoui, S. (2023). A Novel Probabilistic Approach for Detecting Concept Drift in Streaming Data. In: Conte, D., Fred, A., Gusikhin, O., Sansone, C. (eds) Deep Learning Theory and Applications. DeLTA 2023. Communications in Computer and Information Science, vol 1875. Springer, Cham. https://doi.org/10.1007/978-3-031-39059-3_12

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  • DOI: https://doi.org/10.1007/978-3-031-39059-3_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-39058-6

  • Online ISBN: 978-3-031-39059-3

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