DEVDAN: Deep evolving denoising autoencoder

The Denoising Autoencoder (DAE) enhances the flexibility of data stream method in exploiting unlabeled samples. Nonetheless, the feasibility of DAE for data stream analytic deserves in-depth study because it characterizes a fixed network capacity which cannot adapt to rapidly changing environments....

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Veröffentlicht in:Neurocomputing (Amsterdam) Jg. 390; S. 297 - 314
Hauptverfasser: Ashfahani, Andri, Pratama, Mahardhika, Lughofer, Edwin, Ong, Yew-Soon
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 21.05.2020
Schlagworte:
Data streams
Denoising autoencoder
Incremental learning
Denoising autoencoder
Incremental learning
Data streams
ISSN:0925-2312, 1872-8286
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Zusammenfassung:The Denoising Autoencoder (DAE) enhances the flexibility of data stream method in exploiting unlabeled samples. Nonetheless, the feasibility of DAE for data stream analytic deserves in-depth study because it characterizes a fixed network capacity which cannot adapt to rapidly changing environments. Deep evolving denoising autoencoder (DEVDAN), is proposed in this paper. It features an open structure in the generative phase and the discriminative phase where the hidden units can be automatically added and discarded on the fly. The generative phase refines the predictive performance of discriminative model exploiting unlabeled data. Furthermore, DEVDAN is free of the problem-specific threshold and works fully in the single-pass learning fashion. We show that DEVDAN can find competitive network architecture compared with state-of-the-art methods on the classification task using ten prominent datasets simulated under the prequential test-then-train protocol.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2019.07.106