Abstract
The rate of change of drift in a data stream can be of interest. It could show, for example, that a strand of bacteria is becoming more resistant to a drug, or that a machine is becoming unreliable and requires maintenance. While concept drift in data streams has been widely studied, no one has studied the rate of change in concept drift. In this paper we define three new drift types: relative abrupt drift, relative moderate drift and relative gradual drift. We propose a novel algorithm that tracks changes in drift intensity relative to previous drift points within the stream. The algorithm is based on mapping drift patterns to a Gaussian function. Our experimental results show that the algorithm is robust and achieving accuracy levels above 90%.
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Huang, D.T.J., Koh, Y.S., Dobbie, G., Pears, R. (2013). Tracking Drift Types in Changing Data Streams. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds) Advanced Data Mining and Applications. ADMA 2013. Lecture Notes in Computer Science(), vol 8346. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53914-5_7
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DOI: https://doi.org/10.1007/978-3-642-53914-5_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-53913-8
Online ISBN: 978-3-642-53914-5
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