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Concept Drift Handling in Information Systems: Preserving the Validity of Deployed Machine Learning Models

Baier, Lucas

Abstract (englisch):

Predictions computed by supervised machine learning models play a crucial role in a variety of innovative applications in business and industry. Typically, value is generated as soon as these models are deployed and continuously used in information systems of an organization. However, machine learning endeavors predominantly focus on conceiving applications for static situations. In this context, the management of the models’ lifecycle to preserve their effectiveness over time in dynamic environments is still in its infancy.

Therefore, this thesis starts with systematically analyzing the full lifecycle of machine learning applications from an information systems (IS) perspective—and understanding and documenting all choices that have to be made throughout this cycle. On that basis, we then perform a qualitative study via practitioner interviews to map particular challenges in the deployment phase. In this context, we identify concept drift as a particularly important challenge to overcome: Concept drift refers to changes in the environment over time which affect the behavior of a machine learning model. This can have an impact on the model’s prediction quality and its overall utility.
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Volltext §
DOI: 10.5445/IR/1000137245
Veröffentlicht am 09.09.2021
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Karlsruhe Service Research Institute (KSRI)
Publikationstyp Hochschulschrift
Publikationsdatum 09.09.2021
Sprache Englisch
Identifikator KITopen-ID: 1000137245
Verlag Karlsruher Institut für Technologie (KIT)
Umfang xii, 271 S.
Art der Arbeit Dissertation
Fakultät Fakultät für Wirtschaftswissenschaften (WIWI)
Institut Institut für Wirtschaftsinformatik und Marketing (IISM)
Prüfungsdatum 07.07.2021
Referent/Betreuer Satzger, G.
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