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Classification in Non-stationary Environments Using Coresets over Sliding Windows

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 12861)


In non-stationary environments, several constraints require algorithms to be fast, memory-efficient, and highly adaptable. While there are several classifiers of the family of lazy learners and tree classifiers in the streaming context, the application of prototype-based classifiers has not found much attention. Prototype-based classifiers however have some interesting characteristics, which are also useful in streaming environments, in particular being highly interpretable. Hence, we propose a new prototype-based classifier, which is based on Minimum Enclosing Balls over sliding windows. We propose this algorithm as a linear version as well as kernelized. Our experiments show, that this technique can be useful and is comparable in performance to another popular prototype-based streaming classifier – the Adaptive Robust Soft Learning Vector Quantization but with an additional benefit of having a configurable window size to catch rapidly changing drift and the ability to use the internal mechanics for drift detection.

M. Heusinger—Supported by StMWi, project OBerA, grant number IUK-1709-0011// IUK530/010.

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Heusinger, M., Schleif, FM. (2021). Classification in Non-stationary Environments Using Coresets over Sliding Windows. In: Rojas, I., Joya, G., Català, A. (eds) Advances in Computational Intelligence. IWANN 2021. Lecture Notes in Computer Science(), vol 12861. Springer, Cham.

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