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Automated layer-wise solution for ensemble deep randomized feed-forward neural network

delete2022-12-01
delete19
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OA
AI
M
Minghui Hu
R
Ruobin Gao
P
Ponnuthurai Nagaratnam Suganthan *
M
M. Tanveer
DOI:10.1016/j.neucom.2022.09.148delete
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摘要

摘要

En 中文
The randomized feed-forward neural network is a single hidden layer feed-forward neural network that enables efficient learning by optimizing only the output weights. The ensemble deep learning framework significantly improves the performance of randomized neural networks. However, the framework's capa-bilities are limited by traditional hyper-parameter selection approaches. Meanwhile, different random network architectures, such as the existence or lack of a direct link and the mapping of direct links, can also strongly affect the results. We present an automated learning pipeline for the ensemble deep randomized feed-forward neural network in this paper, which integrates hyper-parameter selection and randomized network architectural search via Bayesian optimization to ensure robust performance. Experiments on 46 UCI tabular datasets show that our strategy produces state-of-the-art performance on various tabular datasets among a range of randomized networks and feed-forward neural networks. We also conduct ablation studies to investigate the impact of various hyper-parameters and network architectures.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keyword:
Randomized feed-forward neural network
Random vector functional link
Automated machine learning
Bayesian optimization
Ensemble deep random vector functional
link
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Neurocomputing
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Nanyang Technological University
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indian institute of technology system (iit system)
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Qatar University
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