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Department of Statistics
Research Assistant

Dr. Andrea Bommert


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Phone: +49 231 755 3128

Mathematics, Room 722
Vogelpothsweg 87
44227 Dortmund

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© Andrea Bommert​/​privat
  • Stable Variable Selection
  • Stability Measures
  • Variable Selection
  • Filter Methods for Variable Selection
  • Selection of Correlated Variables
  • Prediction Models
  • Classification
  • Clustering
  • Neutral Comparison Studies
  • High-dimensional Data
  • Bayesian Optimization
  • Optimization of Stochastic Target Functions
  • Multi-Objective Optimization
  • Applications in Logistics
  • Bommert, A. M., Rahnenführer, J., & Lang, M. (2022). Employing an adjusted stability measure for multi-criteria model fitting on data sets with similar features. In G. Nicosia, V. Ohja, E. L. Malfa, G. L. Malfa, & G. Jansen (Hrsg.), Machine learning, optimization, and data science (Verlagsversion, Bd. 13163, S. 81–92). Springer International Publishing.
  • Bommert, A. M., & Lang, M. (2021). stabm: stability measures for feature selection [OnlineRessource]. The Journal of Open Source Software, 6(59), 3010.
  • Bommert, A. M., & Rahnenführer, J. (2021). Adjusted measures for feature selection stability for data sets with similar features. In G. Nicosia, V. Ojha, E. La Malfa, G. Jansen, V. Sciacca, P. Pardalos, G. Giuffrida, & R. Umeton (Hrsg.), Machine learning, optimization, and data science (Verlagsversion, Bd. 12565/12566, S. 203–214). Springer.
  • Bommert, A. M., Welchowski, T., Schmid, M., & Rahnenführer, J. (2021). Benchmark of filter methods for feature selection in high-dimensional gene expression survival data. Briefings in Bioinformatics, 23(1), Article bbab354.
  • Bommert, A. M., Rahnenführer, J., & Weihs, C. (2020). Integration of feature selection stability in model fitting (Verlagsversion) [Universitätsbibliothek Dortmund].
  • Bommert, A. M., Sun, X., Bischl, B., Rahnenführer, J., & Lang, M. (2020). Benchmark for filter methods for feature selection in high-dimensional classification data [OnlineRessource]. Computational Statistics & Data Analysis, 143, 106839.
  • Sun, X., Bommert, A. M., Pfisterer, F., Rahnenführer, J., Lang, M., & Bischl, B. (2019). High dimensional restrictive federated model selection with multi-objective bayesian optimization over shifted distributions [OnlineRessource]. In Y. Bi, R. Bhatia, & S. Kapoor (Hrsg.), Intelligent systems and applications (Verlagsversion, Bd. 1037, S. 629–647). Springer.
  • Bommert, A. M., Rahnenführer, J., & Lang, M. (2017). A multi-criteria approach to find predictive and sparse models with stable feature selection for high-dimensional data. Computational and Mathematical Methods in Medicine, 2017, 1–18.
  • Statistische Verfahren (Winter 2023/24)
  • Einführungskurs in SAS (Winter 2023/24)
  • Einführungskurs in SQL und APIs (Winter 2023/24)
  • Einführung in das statistische Lernen (Summer 2023)
  • Empirische Analysemethoden (Summer 2023)
  • Statistische Verfahren (Winter 2022/23)
  • Einführungskurs in SAS (Winter 2022/23)
  • Einführungskurs in SQL (Winter 2022/23)

Research Experience

  • Since Oct. 2016: Research Assistant at the Department of Statistics
  • Oct. 2013 - Sep. 2016: Student Assistant at the Department of Statistics


  • Dr. rer. nat (Statistics): TU Dortmund University, 2021. Dissertation: Integration of Feature Selection Stability in Model Fitting
  • M. Sc. (Statistics): TU Dortmund University, 2016. Master's thesis: Stabile Variablenselektion in der Klassifikation
  • B. Sc. (Data Analysis and Data Management): TU Dortmund University, 2014. Bachelor's thesis: Robuste Schätzung des Parametervektors bei der linearen Quantilsregression