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  • Jaworski_et_al_ICAISC_2020

    Rights statement: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-030-61401-0_12

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    Embargo ends: 7/10/22

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Concept Drift Detection Using Autoencoders in Data Streams Processing

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Publication date7/10/2020
Host publicationArtificial Intelligence and Soft Computing: 19th International Conference, ICAISC 2020, Zakopane, Poland, October 12-14, 2020, Proceedings, Part I
EditorsLeszek Rutkowski, Rafał Scherer, Marcin Korytkowski, Witold Pedrycz, Ryszard Tadeusiewicz, Jacek M. Zurada
Place of PublicationCham
PublisherSpringer
Pages124-133
Number of pages10
ISBN (Electronic)9783030614010
ISBN (Print)9783030614003
<mark>Original language</mark>English

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume12415
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Abstract

In this paper, the problem of concept drift detection in data stream mining algorithms is considered. The autoencoder is proposed to be applied as a drift detector. The autoencoders are neural networks that are learned how to reconstruct input data. As a side effect, they are able to learn compact nonlinear codes, which summarize the most important features of input data. We suspect that the properly learned autoencoder on one part of the data stream can then be used to monitor possible changes in the following stream parts. The changes are analyzed by monitoring variations of the autoencoder cost function. Two cost functions are applied in this paper: the cross-entropy and the reconstruction error. Preliminary experimental results show that the proposed autoencoder-based detector is able to handle different types of concept drift, e.g. the sudden or the gradual. © 2020, Springer Nature Switzerland AG.

Bibliographic note

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-030-61401-0_12