DEVDAN: Deep evolving denoising autoencoder

Andri Ashfahani, Mahardhika Pratama*, Edwin Lughofer, Yew Soon Ong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

75 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)297-314
Number of pages18
Publication statusPublished - 21 May 2020
Externally publishedYes


  • Data streams
  • Denoising autoencoder
  • Incremental learning


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