2020
DOI: 10.1007/978-3-030-61401-0_12
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Concept Drift Detection Using Autoencoders in Data Streams Processing

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 followin… Show more

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Cited by 17 publications
(4 citation statements)
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“…The drifted data contains 30 batches, where initial 20 batches contain non-drifted data. In the last 10 batches, the distribution of both class data is changed in such a way that in batch number 31 The summary of the datasets used in this research is shown in Table 3. In the case of real-world datasets, it is usually not known whether the drift is present or not, and if it is present, the location of the drift is not known.…”
Section: Varying Distributions (Vd) Datasetmentioning
confidence: 99%
“…The drifted data contains 30 batches, where initial 20 batches contain non-drifted data. In the last 10 batches, the distribution of both class data is changed in such a way that in batch number 31 The summary of the datasets used in this research is shown in Table 3. In the case of real-world datasets, it is usually not known whether the drift is present or not, and if it is present, the location of the drift is not known.…”
Section: Varying Distributions (Vd) Datasetmentioning
confidence: 99%
“…Detects new tasks (i.e., new distribution of features in the input data of knowledge triplets). We use autoencoders [4,34,62]. An auto-encoder is an artificial neural network that learns to encode unlabeled data.…”
Section: Knowledge Managermentioning
confidence: 99%
“…There have been few tentative attempts to develop unsupervised concept drift detection methods by using neural networks. In [12], the authors use the reconstruction error of an autoencoder to detect changes in the data. In [13], they use a contrastive loss to learn a low-dimensional embedding of data.…”
Section: Related Workmentioning
confidence: 99%