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Adaptive Automated Machine Learning

Kulbach, Cedric Peter Charles ORCID iD icon 1
1 Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

The ever-growing demand for machine learning has led to the development of automated machine learning (AutoML) systems that can be used off the shelf by non-experts. Further, the demand for ML applications with high predictive performance exceeds the number of machine learning experts and makes the development of AutoML systems necessary. Automated Machine Learning tackles the problem of finding machine learning models with high predictive performance. Existing approaches incorporating deep learning techniques assume that all data is available at the beginning of the training process (offline learning). They configure and optimise a pipeline of preprocessing, feature engineering, and model selection by choosing suitable hyperparameters in each model pipeline step. Furthermore, they assume that the user is fully aware of the choice and, thus, the consequences of the underlying metric (such as precision, recall, or F1-measure). By variation of this metric, the search for suitable configurations and thus the adaptation of algorithms can be tailored to the user’s needs. With the creation of a vast amount of data from all kinds of sources every day, our capability to process and understand these data sets in a single batch is no longer viable. ... mehr


Volltext §
DOI: 10.5445/IR/1000155322
Veröffentlicht am 02.02.2023
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Hochschulschrift
Publikationsdatum 02.02.2023
Sprache Englisch
Identifikator KITopen-ID: 1000155322
Verlag Karlsruher Institut für Technologie (KIT)
Umfang v, 184 S.
Art der Arbeit Dissertation
Fakultät Fakultät für Wirtschaftswissenschaften (WIWI)
Institut Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Prüfungsdatum 08.09.2022
Referent/Betreuer Sure-Vetter, York
Bifet, Albert
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