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Department of Statistics
Studenten im Sommer auf dem Campus Nord, rote Zahnräder im Hintergrund © Roland Baege​/​TU Dortmund
Postdoc

Dr. Andrea Bommert

Contact

TU Dort­mund Uni­ver­sity
Department of Sta­tis­tics
Statistical Methods in Genetics and Chemometrics
Mathematics Building, Room 722
44221 Dortmund
Germany

E-Mail: bommert [at] statistik.tu-dortmund.de
Phone: +49 231 755 3128

Portrait photo of Andrea Bommert © Andrea Bommert​/​private
  • Dr. rer. nat (Statistics): TU Dortmund University, 2021
    Thesis: Integration of Feature Selection Stability in Model Fitting
  • M. Sc. (Statistics): TU Dortmund University, 2016
    Master thesis: Stabile Variablenselektion in der Klassifikation
  • B. Sc. (Data Science): TU Dortmund University, 2014
    Bachelor thesis: Robuste Schätzung des Parametervektors bei der linearen Quantilsregression
Scientific work
  • since January 2021: Postdoc at the Department of Statistics
  • October 2016 - January 2021:  Research Assistant and doctoral student at the Department of Statistics
  • October 2013 - September 2016:  Student Assistant at the Department of Statistics
  • Stable feature selection
  • Stability measures
  • Feature selection
  • Filter methods for feature selection
  • Selection of correlated features
  • Predicition models
  • Classification
  • Clustering
  • Neutral comparison studies
  • High-dimensional data
  • Bayesian optimization
  • Optimization of stochastic functions
  • Multi-criteria optimization
  • Applications in logistics

ORCiD
Google Schoolar
GitHub

2022

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). Springer International Publishing. https://doi.org/10.1007/978-3-030-95467-3_6

2021

Bommert, A. M., & Lang, M. (2021). stabm: stability measures for feature selection [OnlineRessource]. The Journal of Open Source Software, 6(59), 3010. https://doi.org/10.21105/joss.03010

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. https://doi.org/10.1007/978-3-030-64583-0_19

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. https://doi.org/10.1093/bib/bbab354

2020

Bommert, A. M., Rahnenführer, J., & Weihs, C. (2020). Integration of feature selection stability in model fitting (Verlagsversion) [Universitätsbibliothek Dortmund]. https://doi.org/10.17877/de290r-21906

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. https://doi.org/10.1016/j.csda.2019.106839

2019

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. https://doi.org/10.1007/978-3-030-29516-5_48

2017

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. https://doi.org/10.1155/2017/7907163

R package stabm CRAN GitHub

  • Lecture Empirische Analysemethoden (SuSe 2022)
  • Tutorial for the lecture Klinische Studien (SuSe 2022)
  • Introductory Case Studies (SuSe 2021)
  • Einführungskurs in SAS (WiSe 2020/2021)
  • Fallstudien I (WiSe 2020/2021)
  • Einführungskurs in SAS (WiSe 2019/2020)
  • Einführungskurs in SAS (WiSe 2018/2019)
  • Tutorial for the lecture Bioinformatik (WiSe 2018/2019)
  • Mehrkriterielle Optimierung (SuSe 2018)
  • Tutorial for the lecture Klinische Studien (SuSe 2018)
  • Fallstudien I (WiSe 2017/2018)
  • Tutorial for the lecture Klinische Studien (SuSe 2017)
  • Fallstudien I (WiSe 2016/2017)
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Location & approach

The campus of TU Dort­mund Uni­ver­sity is located close to interstate junction Dort­mund West, where the Sauerlandlinie A45 (Frankfurt-Dort­mund) crosses the Ruhrschnellweg B1 / A40. The best interstate exit to take from A45 is “Dort­mund-Eichlinghofen” (closer to South Cam­pus), and from B1 / A40 “Dort­mund-Dorstfeld” (closer to North Cam­pus). Signs for the uni­ver­si­ty are located at both exits. Also, there is a new exit before you pass over the B1-bridge leading into Dort­mund.

For travelling to the Department of Statistics, convenient parking places can be found at Vogelpothsweg (Gates 21 / 24) or alternatively at the Otto-Hahn-Straße (Gates 28 / 30 / 35).

TU Dort­mund Uni­ver­sity has its own train station (“Dort­mund Uni­ver­si­tät”). From there, suburban trains (S-Bahn) leave for Dort­mund main station (“Dort­mund Hauptbahnhof”) and Düsseldorf main station via the “Düsseldorf Airport Train Station” (take S-Bahn number 1, which leaves every 15 or 30 minutes). The uni­ver­si­ty is easily reached from Bo­chum, Essen, Mülheim an der Ruhr and Duis­burg.

You can also take the bus or subway train from Dort­mund city to the uni­ver­si­ty: From Dort­mund main station, you can take any train bound for the Station “Stadtgarten”, usually lines U41, U45, U 47 and U49. At “Stadtgarten” you switch trains and get on line U42 towards “Hombruch”. Look out for the Station “An der Palmweide”. From the bus stop just across the road, busses bound for TU Dort­mund Uni­ver­sity leave every ten minutes (445, 447 and 462). Another option is to take the subway routes U41, U45, U47 and U49 from Dort­mund main station to the stop “Dort­mund Kampstraße”. From there, take U43 or U44 to the stop “Dort­mund Wittener Straße”. Switch to bus line 447 and get off at “Dort­mund Uni­ver­si­tät S”.

The H-Bahn is one of the hallmarks of TU Dort­mund Uni­ver­sity. There are two stations on North Cam­pus. One (“Dort­mund Uni­ver­si­tät S”) is directly located at the suburban train stop, which connects the uni­ver­si­ty directly with the city of Dort­mund and the rest of the Ruhr Area. Also from this station, there are connections to the “Technologiepark” and (via South Cam­pus) Eichlinghofen. The other station is located at the dining hall at North Cam­pus and offers a direct connection to South Cam­pus every five minutes.

The AirportExpress is a fast and convenient means of transport from Dort­mund Airport (DTM) to Dort­mund Central Station, taking you there in little more than 20 minutes. From Dort­mund Central Station, you can continue to the uni­ver­si­ty campus by interurban railway (S-Bahn). A larger range of in­ter­na­tio­nal flight connections is offered at Düsseldorf Airport (DUS), which is about 60 kilometres away and can be directly reached by S-Bahn from the uni­ver­si­ty station.

Interactive map

The facilities of TU Dortmund University are spread over two campuses, the larger Campus North and the smaller Campus South. Additionally, some areas of the university are located in the adjacent "Technologiepark".

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