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Understanding Concept Drift

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arxiv 1704.00362 v1 pith:CO4C3HOK submitted 2017-04-02 cs.LG

Geoffrey I. Webb , Loong Kuan Lee , François Petitjean , Bart Goethals This is my paper · ORCID
classification cs.LG
keywords driftconceptlearningquantitativereal-worldaccuracyaffectsalong
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Concept drift is a major issue that greatly affects the accuracy and reliability of many real-world applications of machine learning. We argue that to tackle concept drift it is important to develop the capacity to describe and analyze it. We propose tools for this purpose, arguing for the importance of quantitative descriptions of drift in marginal distributions. We present quantitative drift analysis techniques along with methods for communicating their results. We demonstrate their effectiveness by application to three real-world learning tasks.

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Cited by 1 Pith paper

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  1. Adversarial Attacks for Drift Detection

    cs.LG 2024-11 conditional novelty 6.0 of 10

    Two-window drift detectors can be silently evaded by constructing streams whose window averages stay equal while the underlying distribution changes.

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