Evolving Keras Architectures for Sensor Data Analysis

0478496 - ÚI 2018 RIV PL eng C - Conference Paper (international conference)
Vidnerová, Petra - Neruda, Roman
Evolving Keras Architectures for Sensor Data Analysis.
Proceedings of the 2017 Federated Conference on Computer Science and Information Systems. Warszawa: Polish Information Processing Society, 2017 - (Ganzha, M.; Maciaszek, L.; Paprzycki, M.), s. 109-112. Annals of Computer Science and Information Systems, 11. ISBN 978-83-946253-7-5. ISSN 2300-5963.
[FedCSIS 2017. Federated Conference on Computer Science and Information Systems. Prague (CZ), 03.09.2017-06.09.2017]
R&D Projects: GA ČR GA15-18108S
Institutional support: RVO:67985807
Keywords : genetic algorithms * deep neural networks * air pollution prediction
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Deep neural networks enjoy high interest and have become the state-of-art methods in many fields of machine learning recently. Still, there is no easy way for a choice of network architecture. However, the choice of architecture can significantly influence the network performance. This work is the first step towards an automatic architecture design. We propose a genetic algorithm for an optimization of a network architecture. The algorithm is inspired by and designed directly for the Keras library [1] that is one of the most common implementations of deep neural networks. The target application is the prediction of air pollution based on sensor measurements. The proposed algorithm is evaluated on experiments on sensor data and compared to several fixed architectures and support vector regression.
Permanent Link: http://hdl.handle.net/11104/0274610