A Machine Learning Method for Detecting Concept Drift in Data Streams 


Vol. 14,  No. 11, pp. 975-985, Nov.  2025
https://doi.org/10.3745/TKIPS.2025.14.11.975


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  Abstract

In this paper, we propose a method to detect concept drift in data stream by using machine learning techniques. Existing concept drift detecting methods that using convolutional neural networks presuppose unusual situations where labels are provided in the data stream. Also, existing methods does not consider consistency of concept. For that reason, existing methods have a problem in that input of the data stream are falsely detected as a concept drift due to sensitive response even if there is not a large difference. Therefore, we propose a technique to generate labels through machine learning in the usual data streams, and label adjust method that considers the consistency of concepts by quantifying the difference of labels in order to reduce the detection error. Our proposed method patternize inputs of data stream with autoencoder and clustering technique, and train a convolutional neural network model. Since then we detect concept drift by applying label adjust method to the output of model about past and present input.

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[IEEE Style]

D. Kim, J. Kim, H. Lim, "A Machine Learning Method for Detecting Concept Drift in Data Streams," The Transactions of the Korea Information Processing Society, vol. 14, no. 11, pp. 975-985, 2025. DOI: https://doi.org/10.3745/TKIPS.2025.14.11.975.

[ACM Style]

Daewon Kim, Ji-Ho Kim, and Hyo-Sang Lim. 2025. A Machine Learning Method for Detecting Concept Drift in Data Streams. The Transactions of the Korea Information Processing Society, 14, 11, (2025), 975-985. DOI: https://doi.org/10.3745/TKIPS.2025.14.11.975.

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