I am a postdoctoral researcher in the Department of Applied Mathematics, Computer Science and Statistics, in the Computational Web Intelligence (CWI) research group of Chris Cornelis. Before this, I was a doctoral student supervised by Chris Cornelis and Daniel Peralta.
My research interests include fuzzy rough sets, semi-supervised anomaly detection (one-class classification) and classification with missing values.
I've written a library of machine learning algorithms with fuzzy rough sets for python, fuzzy-rough-learn.
Lenz OU, Bollaert H, Cornelis C. A unified weighting framework for evaluating nearest neighbour classification. arXiv. PDF.
Lenz OU, Peralta D, Cornelis C. Polar encoding: a simple baseline approach for classification with missing values. arXiv. PDF.
Lenz OU, Peralta D, Cornelis C. No imputation without representation. arXiv. PDF. Presented at the DMLR workshop of ICML 2023, as well as BNAIC/BeNeLearn 2023.
Lenz OU, Cornelis C (2023). Classifying token frequencies using angular Minkowski $p$-distance. Proceedings of IJCRS 2023, to appear. PDF.
Lenz OU (2023). Fuzzy rough nearest classification on real-life datasets. Doctoral thesis in computer science, Universiteit Gent. PDF.
Lenz OU, Cornelis C, Peralta D (2022). fuzzy-rough-learn 0.2 : a Python library for fuzzy rough set algorithms and one-class classification. Proceedings of FUZZ-IEEE 2022. doi: 10.1109/FUZZ-IEEE55066.2022.9882778. PDF.
Lenz OU, Peralta D, Cornelis C (2022). Optimised one-class classification performance. Machine Learning, vol 111, no 8, pp 2863–2883. doi: 10.1007/s10994-022-06147-2. PDF.
Lenz OU, Peralta D, Cornelis C (2021). Adapting fuzzy rough sets for classification with missing values. Proceedings of IJCRS 2021, pp 192–200. doi: 10.1007/978-3-030-87334-9_16. PDF.
Lenz OU, Peralta D, Cornelis C (2021). Average Localised Proximity: A new data descriptor with good default one-class classification performance. Pattern Recognition, vol 118, no 107991. doi: 10.1016/j.patcog.2021.107991. PDF.
Lenz OU, Peralta D, Cornelis C (2020). Scalable approximate FRNN-OWA classification. IEEE Transactions on Fuzzy Systems, vol 28, no 5, pp 929–938. doi: 10.1109/TFUZZ.2019.2949769. PDF.
Lenz OU, Peralta D, Cornelis C (2020). fuzzy-rough-learn 0.1 : a Python library for machine learning with fuzzy rough sets. Proceedings of IJCRS 2020, pp 491–499. doi: 10.1007/978-3-030-52705-1_36. PDF.
Lenz OU, Peralta D, Cornelis C (2019). A scalable approach to fuzzy rough nearest neighbour classification with ordered weighted averaging operators. Proceedings of IJCRS 2019, pp 197–209. doi: 10.1007/978-3-030-22815-6_16. PDF.
Lenz OU (2012). Case we can believe in: a radically minimalist proposal. MA thesis in linguistics, Universiteit Leiden, under Prof Dr Johan ECV Rooryck. PDF. An extensive argument against Case in Chomskyan syntax.
Lenz OU (2011). The classifying space of a monoid. MSc thesis in mathematics, Universiteit Leiden & Università degli Studi di Padova, under Dr Lenny DJ Taelman. PDF. Establishes the classifying space of commutative monoids and free monoids.
Theerens A, Lenz OU, Cornelis C (2022). Choquet-based fuzzy rough sets. International Journal of Approximate Reasoning, vol 146, pp 62–78. doi: 10.1016/j.ijar.2022.04.006.