EvoAAA: An evolutionary methodology for automated neural autoencoder architecture search

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Abstract
Machine learning models work better when curated features are provided to them. Feature engineering methods have been usually used as a preprocessing step to obtain or build a proper feature set. In late years, autoencoders (a specific type of symmetrical neural network) have been widely used to perform representation learning, proving their competitiveness against classical feature engineering algorithms. The main obstacle in the use of autoencoders is finding a good architecture, a process that most experts confront manually. An automated autoencoder symmetrical architecture search procedure, based on evolutionary methods, is proposed in this paper. The methodology is tested against nine heterogeneous data sets. The obtained results show the ability of this approach to find better architectures, able to concentrate most of the useful information in a minimized encoding, in a reduced time.
Year of Publication
2020
Journal
Integrated Computer-Aided Engineering
Volume
27
Number of Pages
211-231
Date Published
05/2020
DOI
10.3233/ICA-200619
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