An end-to-end process model for supervised machine learning classification: from problem to deployment in information systems

Simple item page
dc.contributor.authorHirt, Robin
dc.contributor.authorKoehl, Niklas J.
dc.contributor.authorSatzger, Gerhard
dc.contributor.editorMaedche, Alexander
dc.contributor.editorvom Brocke, Jan
dc.contributor.editorHevner, Alan
dc.date.accessioned2017-08-11T08:39:01Z
dc.date.available2017-08-11T08:39:01Z
dc.date.issued2017
dc.description.abstractExtracting meaningful knowledge from (big) data represents a key success factor in many industries today. Supervised machine learning (SML) has emerged as a popular technique to learn patterns in complex data sets and to identify hidden correlations. When this insight is turned into action, business value is created. However, common data mining processes are generally not tailored to SML. In addition, they fall short of providing an end-to-end view that not only supports building a ”one off” model, but also covers its operational deployment within an information system. In this research-in-progress work we apply a Design Science Research (DSR) approach to develop a SML process model artifact that comprises model initiation, error estimation and deployment. In a first cycle, we evaluate the artifact in an illustrative scenario to demonstrate suitability. The results encourage us to further refine the approach and to prepare evaluations in concrete use cases. Thus, we move towards contributing a general process model that supports the systematic design of machine learning solutions to turn insights into continuous action.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationHirt, R., Kuehl, N. J. and Satzger, G. (2017) 'An End-to-End Process Model for Supervised Machine Learning Classification: From Problem to Deployment in Information Systems'. In: Maedche, A., vom Brocke, J., Hevner, A. (eds.) Designing the Digital Transformation: DESRIST 2017 Research in Progress Proceedings of the 12th International Conference on Design Science Research in Information Systems and Technology. Karlsruhe, Germany. 30 May - 1 Jun. Karslruhe: Karlsruher Institut für Technologie (KIT), pp. 55-63en
dc.identifier.endpage63
dc.identifier.issn2194-1629
dc.identifier.startpage55
dc.identifier.urihttps://hdl.handle.net/10468/4442
dc.language.isoenen
dc.publisherKarlsruher Institut für Technologie (KIT)en
dc.relation.ispartofDesigning the Digital Transformation: DESRIST 2017 Research in Progress Proceedings of the 12th International Conference on Design Science Research in Information Systems and Technology. Karlsruhe, Germany. 30 May - 1 Jun.
dc.relation.urihttps://publikationen.bibliothek.kit.edu/1000069452
dc.relation.urihttp://desrist2017.kit.edu/
dc.rights©2017, The Author(s). This document is licensed under the Creative Commons Attribution – Share Alike 4.0 International License (CC BY-SA 4.0): https://creativecommons.org/licenses/by-sa/4.0/deed.enen
dc.rights.urihttps://creativecommons.org/licenses/by-sa/4.0/deed.en
dc.subjectData mining processen
dc.subjectSupervised machine learningen
dc.subjectInformation systemsen
dc.titleAn end-to-end process model for supervised machine learning classification: from problem to deployment in information systemsen
dc.typeConference itemen
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
3489.pdf
Size:
625.45 KB
Format:
Adobe Portable Document Format
Description:
Published Version
Download
Collections
Designing the Digital Transformation: DESRIST 2017 Research-in-Progress Proceedings
Pharmacy - Conference Items