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Enhanced neural architecture search using super learner and ensemble approaches

Lankford, Séamus and Grimes, Diarmuid orcid logoORCID: 0000-0001-5551-6504 (2021) Enhanced neural architecture search using super learner and ensemble approaches. In: 2nd Asia Service Sciences and Software Engineering Conference (ASSE '21: 2021), 24 - 26 Feb 2021, Macau, Macao. ISBN 978-1-4503-8908-2

Abstract
Neural networks, and in particular Convolutional Neural Networks (CNNs), are often optimized using default parameters. Neural Architecture Search (NAS) enables multiple architectures to be evaluated prior to selection of the optimal architecture. A system integrating open-source tools for Neural Architecture Search (OpenNAS) of image classification problems has been developed and made available to the open-source community. OpenNAS takes any dataset of grayscale, or RGB images, and generates the optimal CNN architecture. The training and optimization of neural networks, using super learner and ensemble approaches, is explored in this research. Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and pretrained models serve as base learners for network ensembles. Meta learner algorithms are subsequently applied to these base learners and the ensemble performance on image classification problems is evaluated. Our results show that a stacked generalization ensemble of heterogeneous models is the most effective approach to image classification within OpenNAS.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: 2nd Asia Service Sciences and Software Engineering Conference, Proceedings. . Association for Computing Machinery (ACM). ISBN 978-1-4503-8908-2
Publisher:Association for Computing Machinery (ACM)
Official URL:https://doi.org/10.1145/3456126.3456133
Copyright Information:© 2021 The Authors.
Funders:Science Foundation Ireland (SFI) Research Centres Programme (Grant 13/RC/2016), European Regional Development Fund, Munster Technological University
ID Code:28346
Deposited On:23 May 2023 08:39 by Seamus Lankford . Last Modified 23 May 2023 08:39
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