Contact
Universitätsstraße 38
70569 Stuttgart
Germany
Room: 2.365
Office Hours
On appointment
Subject
Working Area: Machine Learning, Ensemble Learning, Classification
In my PhD topic " Deployment and data-driven fusion of heterogeneous predictive models" I deal with the creation of optimized ensembles, i.e. the combination of multiple classification models. For this purpose, the influence of data, model diversity, etc. on ensembles shall be investigated. Furthermore, an automated procedure shall be developed to create optimized ensembles based on the discovered connections.
Citation Metrics
- Google Scholar: Profile
- ORCID: 0000-0003-4808-1922
- Research Gate: Profile
Publications
2023
Julius Voggesberger. "Optimierung von Klassifikator-Ensembles mit AutoML". In: GvDB. 2023.
Julius Voggesberger, Peter Reimann und Bernhard Mitschang. “Towards the Automatic Creation of Optimized Classifier Ensembles”. In: Proc. of the 25th Int. Conference on Enterprise Information Systems (ICEIS) – Volume 1. 2023, S. 614–621.
Michael Behringer, Dennis Treder-Tschechlov, Julius Voggesberger, Pascal Hirmer und Bernhard Mitschang u. a. “SDRank: A Deep Learning Approach for Similarity Ranking of Data Sources to Support User-Centric Data Analysis”. In: Proc. of the 25th Int. Conference on Enterprise Information Systems (ICEIS) – Volume 1. 2023, S. 419–428.
Thesis
J. Voggesberger, "AutoML für Ensembles: Erhöhung der Klassifikationsperformanz durch Optimierung der Modelldiversität und der Entscheidungsfusion"
Masterthesis. 2022.
J. Voggesberger, " Evaluation von Zwischenergebnissen in Entscheidungsbäumen"
Bachelorthesis. 2019.
Summer Term 2023
- Advanced Seminar "Data Management for End-to-End Machine Learning"
Supervision
Winter Term 2022/2023
- Seminar "Entwicklung von Data-Science-Anwendungen"
Supervision
Student Projects in Progress
- Master Thesis:
Meta-Learning for Classifier Ensemble Optimization - Master Thesis:
Development of a method for data-driven prediction of the outputs of a cold forging simulation