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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sg-ntu-dr.10356-1609722022-08-10T01:59:53Z DEVDAN: Deep evolving denoising autoencoder Ashfahani, Andri Pratama, Mahardhika Lughofer, Edwin Ong, Yew Soon School of Computer Science and Engineering Engineering::Computer science and engineering Denoising Autoencoder Data Streams 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. The third author acknowledges the support by the LCM K2 Cen- ter within the framework of the Austrian COMET-K2 program. 2022-08-10T01:59:53Z 2022-08-10T01:59:53Z 2020 Journal Article Ashfahani, A., Pratama, M., Lughofer, E. & Ong, Y. S. (2020). DEVDAN: Deep evolving denoising autoencoder. Neurocomputing, 390, 297-314. https://dx.doi.org/10.1016/j.neucom.2019.07.106 0925-2312 https://hdl.handle.net/10356/160972 10.1016/j.neucom.2019.07.106 2-s2.0-85073050094 390 297 314 en Neurocomputing © 2019 Elsevier B.V. All rights reserved. |
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Engineering::Computer science and engineering Denoising Autoencoder Data Streams Ashfahani, Andri Pratama, Mahardhika Lughofer, Edwin Ong, Yew Soon DEVDAN: Deep evolving denoising autoencoder |
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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. |
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School of Computer Science and Engineering |
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School of Computer Science and Engineering Ashfahani, Andri Pratama, Mahardhika Lughofer, Edwin Ong, Yew Soon |
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Article |
author |
Ashfahani, Andri Pratama, Mahardhika Lughofer, Edwin Ong, Yew Soon |
author_sort |
Ashfahani, Andri |
title |
DEVDAN: Deep evolving denoising autoencoder |
title_short |
DEVDAN: Deep evolving denoising autoencoder |
title_full |
DEVDAN: Deep evolving denoising autoencoder |
title_fullStr |
DEVDAN: Deep evolving denoising autoencoder |
title_full_unstemmed |
DEVDAN: Deep evolving denoising autoencoder |
title_sort |
devdan: deep evolving denoising autoencoder |
publishDate |
2022 |
url |
https://hdl.handle.net/10356/160972 |
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