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http://dx.doi.org/10.18419/opus-9581
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DC Element | Wert | Sprache |
---|---|---|
dc.contributor.author | Volga, Yuliya | - |
dc.date.accessioned | 2018-02-02T14:36:56Z | - |
dc.date.available | 2018-02-02T14:36:56Z | - |
dc.date.issued | 2017 | de |
dc.identifier.other | 50041680X | - |
dc.identifier.uri | http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-95984 | de |
dc.identifier.uri | http://elib.uni-stuttgart.de/handle/11682/9598 | - |
dc.identifier.uri | http://dx.doi.org/10.18419/opus-9581 | - |
dc.description.abstract | The production process on a factory can be described by big amount of data. It is used to optimize the production process, reduce number of failures and control material waste. For this, data is processed, analyzed and classified using the analysis techniques - text classification algorithms. Thus there should be an approach that supports choice of algorithms on both, technical and management levels. We propose a tool called Analytics Configuration Performance Dashboard which facilitates process of algorithm configurations comparison. It is based on a meta-learning approach. Additionally, we introduce three business metrics on which algorithms are compared, they map onto machine learning algorithm evaluation metrics and help to assess algorithms from industry perspective. Moreover, we develop a visualization in order to provide clear representation of the data. Clustering is used to define groups of algorithms that have common performance in business metrics. We conclude with evaluation of the proposed approach and techniques, which were chosen for its implementation. | en |
dc.language.iso | en | de |
dc.rights | info:eu-repo/semantics/openAccess | de |
dc.subject.ddc | 004 | de |
dc.title | ACP Dashboard: an interactive visualization tool for selecting analytics configurations in an industrial setting | en |
dc.type | masterThesis | de |
ubs.fakultaet | Informatik, Elektrotechnik und Informationstechnik | de |
ubs.institut | Institut für Parallele und Verteilte Systeme | de |
ubs.publikation.seiten | 73 | de |
ubs.publikation.typ | Abschlussarbeit (Master) | de |
Enthalten in den Sammlungen: | 05 Fakultät Informatik, Elektrotechnik und Informationstechnik |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
---|---|---|---|---|
Mater Thesis. 12 December 2017. Yuliya Volga.pdf | 2,23 MB | Adobe PDF | Öffnen/Anzeigen |
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