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A self-organizing ensemble of deep neural networks for the classification of data from complex processes
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-2128-7090
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0001-8884-2154
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0001-7106-0025
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-2973-3112
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

We present a new self-organizing algorithm for classification of a data that combines and extends the strengths of several common machine learning algorithms, such as algorithms in self-organizing neural networks, ensemble methods and deep neural networks. The increased expression power is combined with the explanation power of self-organizing networks. Our algorithm outperforms both deep neural networks and ensembles of deep neural networks. For our evaluation case, we use production monitoring data from a complex steel manufacturing process, where data is both high-dimensional and has many nonlinear interdependencies. In addition to the improved prediction score, the algorithm offers a new deep-learning based approach for how computational resources can be focused in data exploration, since the algorithm points out areas of the input space that are more challenging to learn.

Place, publisher, year, edition, pages
2018. Vol. 855, p. 248-259
Series
Communications in Computer and Information Science, ISSN 1865-0929
Keywords [en]
artificial neural networks, complex processes, ensemble methods, self organisation
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
URN: urn:nbn:se:his:diva-15008DOI: 10.1007/978-3-319-91479-4_21Scopus ID: 2-s2.0-85048061876OAI: oai:DiVA.org:his-15008DiVA, id: diva2:1195203
Conference
17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Jun 11 - Jun 15, 2018, Cadiz, Spain
Available from: 2018-04-04 Created: 2018-04-04 Last updated: 2018-07-27Bibliographically approved

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Ståhl, NiclasFalkman, GöranMathiason, GunnarKarlsson, Alexander

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