| Transparency, auditability, and explainability of machine learning models in credit scoring M Bücker, G Szepannek, A Gosiewska, P Biecek Journal of the Operational Research Society 73 (1), 70-90, 2022 | 139 | 2022 |
| ClustMixType: User-Friendly Clustering of Mixed-Type Data in R. G Szepannek R J. 10 (2), 200, 2018 | 98 | 2018 |
| klaR–Classification and Visualization C Roever, N Raabe, K Luebke, U Ligges, G Szepannek, M Zentgraf, ... R package, Version 0.3-3. Available from http://cran. R-project. org, 2004 | 63* | 2004 |
| Why we should teach causal inference: Examples in linear regression with simulated data K Lübke, M Gehrke, J Horst, G Szepannek Journal of Statistics Education 28 (2), 133-139, 2020 | 48 | 2020 |
| Facing the challenges of developing fair risk scoring models G Szepannek, K Lübke Frontiers in Artificial Intelligence 4, 681915, 2021 | 30 | 2021 |
| Cluster validation for mixed-type data R Aschenbruck, G Szepannek Archives of Data Science, Series A 6 (1), 02, 2020 | 26 | 2020 |
| On class imbalance correction for classification algorithms in credit scoring B Bischl, T Kühn, G Szepannek Operations Research Proceedings 2014: Selected Papers of the Annual …, 2016 | 26 | 2016 |
| On the combination of locally optimal pairwise classifiers G Szepannek, B Bischl, C Weihs Engineering Applications of Artificial Intelligence 22 (1), 79-85, 2009 | 19 | 2009 |
| How much do we see? On the explainability of partial dependence plots for credit risk scoring G Szepannek, K Lübke Argumenta Oeconomica, 2023 | 18* | 2023 |
| clustmixtype: K-prototypes clustering for mixed variable-type data G Szepannek, R Aschenbruck R package version 0.1-16. URL https://cran. r-project. org/package= clustMixType, 2016 | 17* | 2016 |
| On the practical relevance of modern machine learning algorithms for credit scoring applications G Szepannek WIAS Report Series 29, 88-96, 2017 | 15 | 2017 |
| Distances in classification C Weihs, G Szepannek Industrial Conference on Data Mining, 1-12, 2009 | 15 | 2009 |
| Local models in register classification by timbre C Weihs, G Szepannek, U Ligges, K Luebke, N Raabe Data science and classification, 315-322, 2006 | 15* | 2006 |
| An Overview on the landscape of R packages for open source scorecard modelling G Szepannek Risks 10 (3), 67, 2022 | 14* | 2022 |
| Perceptually based phoneme recognition in popular music G Szepannek, M Gruhne, B Bischl, S Krey, T Harczos, F Klefenz, ... Classification as a Tool for Research: Proceedings of the 11th IFCS Biennial …, 2010 | 14 | 2010 |
| Imputation strategies for clustering mixed-type data with missing values R Aschenbruck, G Szepannek, AFX Wilhelm Journal of Classification 40 (1), 2-24, 2023 | 13 | 2023 |
| Local modelling in classification G Szepannek, J Schiffner, J Wilson, C Weihs Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and …, 2008 | 13 | 2008 |
| Towards automatic speech recognition based on cochlear traveling wave delay trajectories T Harczos, G Szepannek, F Klefenz Proceedings of the International Symposium on Auditory and Audiological …, 2007 | 13 | 2007 |
| Local modelling in classification on different feature subspaces G Szepannek, C Weihs Industrial conference on data mining, 226-238, 2006 | 12 | 2006 |
| Explaining Artificial Intelligence with Care: Analyzing the Explainability of Black Box Multiclass Machine Learning Models in Forensics G Szepannek, K Lübke KI-Künstliche Intelligenz 36 (2), 125-134, 2022 | 11 | 2022 |
