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Probabilistic Neural Networks for the Streaming Data Classification

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Stream Data Mining: Algorithms and Their Probabilistic Properties

Part of the book series: Studies in Big Data ((SBD,volume 56))

Abstract

Among the data stream mining algorithms proposed so far in the literature most of them are devoted mainly to the data classification task [1,2,3]. Although there exist a lot of methods for classification of static datasets, they can hardly be adapted to deal with data streams. This is due to the features of the data stream such as potentially infinite volume, fast rate of data arrival and the occurrence of concept drift.

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Authors and Affiliations

  1. Institute of Computational Intelligence, Czestochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski, Maciej Jaworski & Piotr Duda

  2. Information Technology Institute, University of Social Sciences, Lodz, Poland

    Leszek Rutkowski

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  1. Leszek Rutkowski
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  2. Maciej Jaworski
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  3. Piotr Duda
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Correspondence to Leszek Rutkowski .

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Rutkowski, L., Jaworski, M., Duda, P. (2020). Probabilistic Neural Networks for the Streaming Data Classification. In: Stream Data Mining: Algorithms and Their Probabilistic Properties. Studies in Big Data, vol 56. Springer, Cham. https://doi.org/10.1007/978-3-030-13962-9_11

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