Abstract
This paper addresses the issue of data stream mining using the Restricted Boltzmann Machine (RBM). Recently, it was demonstrated that the RBM can be useful as a concept drift detector in data streams with time-changing probability density. In this paper, we consider another problem which often occurs in real-life data streams, i.e. incomplete data. We propose two modifications of the RBM learning algorithms to make them able to handle missing values. The first one inserts an additional procedure before the positive phase of the Contrastive Divergence. This procedure aims at inferring the missing values in the visible layer by performing a fixed number of Gibbs steps. The second modification introduces dimension-dependent sizes of minibatches in the stochastic gradient descent method. The proposed methods are verified experimentally, demonstrating their usability for concept drift detection in data streams with incomplete data.
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Acknowledgments
The project financed under the program of the Minister of Science and Higher Education under the name “Regional Initiative of Excellence” in the years 2019–2022 project number 020/RID/2018/19, the amount of financing 12,000,000 PLN. This work was also supported by the Polish National Science Centre under grant no. 2017/27/B/ST6/02852.
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Jaworski, M., Duda, P., Rutkowska, D., Rutkowski, L. (2019). On Handling Missing Values in Data Stream Mining Algorithms Based on the Restricted Boltzmann Machine. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1143. Springer, Cham. https://doi.org/10.1007/978-3-030-36802-9_37
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DOI: https://doi.org/10.1007/978-3-030-36802-9_37
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