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Communication Dans Un Congrès Année : 2022

Experimental analysis on dissimilarity metrics and sudden concept drift detection

Résumé

Learning from non-stationary data presents several new challenges. Among them, a significant problem comes from the sudden changes in the incoming data distributions, the so-called concept drift. Several concept drift detection methods exist, generally based on distances between distributions, either arbitrarily selected or context-dependent. This paper presents a straightforward approach for detecting concept drift based on a weighted dissimilarity metric over posterior probabilities. We also evaluate the performance of three well-known dissimilarity metrics when used by the proposed approach. Experimental evaluation has been done over ten datasets with injected sudden drifts in a binary classification context. Our results first suggest choosing the Kullback-Leibler divergence, and second, they show that our drift detection procedure based on dissimilarity measures is pretty efficient.
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Dates et versions

hal-03898901 , version 1 (14-12-2022)

Identifiants

  • HAL Id : hal-03898901 , version 1

Citer

Gerardo Rubino, Sebastián Basterrech, Jan Platoš, Michał Woźniak. Experimental analysis on dissimilarity metrics and sudden concept drift detection. ISDA 2022 - 22nd International Conference on Intelligent Systems Design and Applications, Dec 2022, virtual, United States. pp.1-8. ⟨hal-03898901⟩
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