Dennis Treder-Tschechlov

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Holger SchwarzIPVS, University of StuttgartVerified email at ipvs.uni-stuttgart.de
Manuel FritzData Analytics @ Zeiss SMTVerified email at ipvs.uni-stuttgart.de
Bernhard MitschangUniversität StuttgartVerified email at ipvs.uni-stuttgart.de
Alfred ZimmermannReutlingen UniversityVerified email at online.de
Tobias BoceckVerified email at stud.uni-stuttgart.de
Justus BognerAssistant Professor, Vrije Universiteit Amsterdam, S2 GroupVerified email at vu.nl
Stefan WagnerProfessor of Software Engineering, Technical University of MunichVerified email at tum.de
Michael BehringerUniversität StuttgartVerified email at michaelbehringer.de
Peter ReimannResearch Group Leader, University of Stuttgart - GSaME, GermanyVerified email at gsame.uni-stuttgart.de
Pascal HirmerResearch Engineer, Mercedes-Benz AGVerified email at ipvs.uni-stuttgart.de

Dennis Treder-Tschechlov

Ph. D. Student, University of Stuttgart

Verified email at ipvs.uni-stuttgart.de - Homepage

Clustering AutoML Meta-Learning Classification Synthetic Data Generation


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Towards a collaborative repository for the documentation of service-based antipatterns and bad smells J Bogner, T Boceck, M Popp, D Tschechlov, S Wagner, A Zimmermann 2019 IEEE International Conference on Software Architecture Companion (ICSA …, 2019	42	2019
AutoML4Clust: Efficient AutoML for Clustering Analyses. D Tschechlov, M Fritz, H Schwarz EDBT, 343-348, 2021	17	2021
Exploiting domain knowledge to address class imbalance and a heterogeneous feature space in multi-class classification V Hirsch, P Reimann, D Treder-Tschechlov, H Schwarz, B Mitschang The VLDB Journal 32 (5), 1037-1064, 2023	3	2023
Approach to synthetic data generation for imbalanced multi-class problems with heterogeneous groups D Treder-Tschechlov, P Reimann, H Schwarz, B Mitschang BTW 2023, 2023	3	2023
Increasing Explainability of Clustering Results for Domain Experts by Identifying Meaningful Features. M Behringer, P Hirmer, D Tschechlov, B Mitschang ICEIS (2), 364-373, 2022	3	2022
Learning from past observations: Meta-learning for efficient clustering analyses M Fritz, D Tschechlov, H Schwarz Big Data Analytics and Knowledge Discovery: 22nd International Conference …, 2020	3	2020
Efficient exploratory clustering analyses in large-scale exploration processes M Fritz, M Behringer, D Tschechlov, H Schwarz The VLDB Journal 31 (4), 711-732, 2022	2	2022
Analysis and transfer of automl concepts for clustering algorithms D Tschechlov	2	2019
ML2DAC: Meta-Learning to Democratize AutoML for Clustering Analysis D Treder-Tschechlov, M Fritz, H Schwarz, B Mitschang Proceedings of the ACM on Management of Data 1 (2), 1-26, 2023		2023
SDRank: A Deep Learning Approach for Similarity Ranking of Data Sources to Support User-Centric Data Analysis. M Behringer, D Treder-Tschechlov, J Voggesberger, P Hirmer, ... ICEIS (1), 419-428, 2023		2023
Metriken zur Evaluation von Teilschritten in Data Mining Analysen D Tschechlov		2017

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