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Detecting feature interactions in agricultural trade data using a deep neural network

O'Donoghue, Jim, Roantree, Mark and McCarren, Andrew orcid logoORCID: 0000-0002-7297-0984 (2017) Detecting feature interactions in agricultural trade data using a deep neural network. In: Big Data Analytics and Knowledge Discovery - DaWaK 2017, 28 - 31 Aug, 2017, Lyon, France. ISBN 978-3-319-64282-6

Agri-analytics is an emerging sector which uses data mining to inform decision making in the agricultural sector. Machine learning is used to accomplish data mining tasks such as prediction, known as predictive analytics in the commercial context. Similar to other domains, hidden trends and events in agri-data can be difficult to detect with traditional machine learning approaches. Deep learning uses architectures made up of many levels of non-linear operations to construct a more holistic model for learning. In this work, we use deep learning for unsupervised modelling of commodity price data in agri-datasets. Specifically, we detect how appropriate input signals contribute to, and interact in, complex deep architectures. To achieve this, we provide a novel extension to a method which determines the contribution of each input feature to shallow, supervised neural networks. Our generalisation allows us to examine deep supervised and unsupervised neural networks.
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Uncontrolled Keywords:Deep learning; data mining; Agri-analytics; deep architecture; big data;
Subjects:Computer Science > Machine learning
DCU Faculties and Centres:Research Institutes and Centres > INSIGHT Centre for Data Analytics
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in: Bellatreche, Ladjel and Chakravarthy, Sharma, (eds.) International Conference on Big Data Analytics and Knowledge Discovery. Lecture Notes in Computer Science book series (LNCS) 10440. Springer. ISBN 978-3-319-64282-6
Official URL:https://doi.org/10.1007/978-3-319-64283-3_33
Copyright Information:© 2017 Springer International Publishing
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland grant no. SFI/12/RC/2289
ID Code:21859
Deposited On:25 Aug 2017 10:34 by Mark Roantree . Last Modified 26 Jun 2019 10:35

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