Abstract
Classifier ensembles have shown the ability to classify drifted data streams. The following paper proposes an ensemble consisting of a single hidden layer feedforward neural network and an Extreme Learning Machine. For this purpose, a new incremental version of the Extreme Learning Machine is also proposed. Motivations behind such an approach have been precisely described and supported by conducted research. The achieved results show when the architecture might be most useful and what are the possible directions for future development of this method.
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Acknowledgement
This work was supported by the statutory funds of the Department of Systems and Computer Networks, Faculty of Information and Communication Technology, Wroclaw University of Science and Technology.
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Wojtachnia, K., Komorniczak, J., Ksieniewicz, P. (2023). Incremental Extreme Learning Machine for Binary Data Stream Classification. In: Burduk, R., Choraś, M., Kozik, R., Ksieniewicz, P., Marciniak, T., Trajdos, P. (eds) Progress on Pattern Classification, Image Processing and Communications. CORES IP&C 2023 2023. Lecture Notes in Networks and Systems, vol 766. Springer, Cham. https://doi.org/10.1007/978-3-031-41630-9_4
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DOI: https://doi.org/10.1007/978-3-031-41630-9_4
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