Abstract
In mining data streams, one of the most challenging tasks is adapting to concept change, that is change over time of the underlying concept in the data. In this paper, we propose a novel ensemble framework for mining concept-changing data streams. This algorithm, called QACC (Quick Adaptation to Changing Concepts), realizes quick adaptation to changing concepts using an ensemble of classifiers. For quick adaptation, QACC sensitively detects concept changes in noisy streaming data. Empirical studies show that the QACC algorithm is efficient for various concept changes.
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Yasumura, Y., Kitani, N., Uehara, K. (2007). Quick Adaptation to Changing Concepts by Sensitive Detection. In: Okuno, H.G., Ali, M. (eds) New Trends in Applied Artificial Intelligence. IEA/AIE 2007. Lecture Notes in Computer Science(), vol 4570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73325-6_85
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DOI: https://doi.org/10.1007/978-3-540-73325-6_85
Publisher Name: Springer, Berlin, Heidelberg
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