Application of a Genetic Algorithm to Variable Selection in Fuzzy Clustering. - Christian Röver; Gero Szepannek (GfKl, 2004)

Different Subspace Classification. - Gero Szepannek; Karsten Luebke (GfKl, 2004)

Variable Selection for Discrimination of More Than Two Classes Where Data are Sparse. - Gero Szepannek; Claus Weihs (GfKl, 2005)

Understanding Patterns with Different Subspace Classification. - Gero Szepannek; Karsten Luebke; Claus Weihs (MLDM, 2005)

Schallanalyse Neuronale Repräsentation des Hörvorgangs als Basis. - Gero Szepannek; Frank Klefenz; Claus Weihs (Inform. Spektrum, 2005)

Vowel Classification by a Neurophysiologically Parameterized Auditory Model. - Gero Szepannek; Tamás Harczos; Frank Klefenz; András Kátai; Patrick Schikowski; Claus Weihs (GfKl, 2006)

Local Modelling in Classification on Different Feature Subspaces. - Gero Szepannek; Claus Weihs (ICDM, 2006)

Local Models in Register Classification by Timbre. - Claus Weihs; Gero Szepannek; Uwe Ligges; Karsten Luebke; Nils Raabe (Data Science and Classification, 2006)

Localized Logistic Regression for Categorical Influential Factors. - Julia Schiffner; Gero Szepannek; Thierry Monthé; Claus Weihs (GfKl, 2008)

Local Modelling in Classification. - Gero Szepannek; Julia Schiffner; Julie Wilson; Claus Weihs (ICDM, 2008)

Extending features for automatic speech recognition by means of auditory modelling. - Gero Szepannek; Tamás Harczos; Frank Klefenz; Claus Weihs (EUSIPCO, 2009)

Distances in Classification. - Claus Weihs; Gero Szepannek (Trans. Case Based Reason., 2009)

On the combination of locally optimal pairwise classifiers. - Gero Szepannek; Bernd Bischl; Claus Weihs (Eng. Appl. Artif. Intell., 2009)

On Class Imbalance Correction for Classification Algorithms in Credit Scoring. - Bernd Bischl; Tobias Kühn; Gero Szepannek (OR, 2014)

Editorial. - Gero Szepannek (Trans. Case Based Reason., 2016)

clustMixType: User-Friendly Clustering of Mixed-Type Data in R. - Gero Szepannek (R J., 2018)

How Much Can We See? A Note on Quantifying Explainability of Machine Learning Models. - Gero Szepannek (CoRR, 2019)

An Overview on the Landscape of R Packages for Credit Scoring. - Gero Szepannek (CoRR, 2020)

Facing the Challenges of Developing Fair Risk Scoring Models. - Gero Szepannek; Karsten Luebke (Frontiers Artif. Intell., 2021)

Teaching Machine Learning with mlr3 using Shiny. - Gero Szepannek; Laurens Martin Tetzlaff; Alexander Frahm; Karsten Lübke (Teaching ML, 2022)

Transparency, auditability, and explainability of machine learning models in credit scoring. - Michael Bücker; Gero Szepannek; Alicja Gosiewska; Przemyslaw Biecek (J. Oper. Res. Soc., 2022)

Explaining Artificial Intelligence with Care. - Gero Szepannek; Karsten Lübke (Künstliche Intell., 2022)

mlr3shiny - State-of-the-art machine learning made easy. - Laurens Martin Tetzlaff; Gero Szepannek (SoftwareX, 2022)

A Survey on the Current Status of Research Data Management in Mecklenburg-Vorpommern Preliminary Results for a Questionnaire Study Among Researchers. - Max Schröder; Sascha Genehr; Rüdiger Köhling; Stefan Schmidt; Ralf Schneider; Sascha Spors; Gero Szepannek; Dagmar Waltemath; Frank Krüger 001 (CoRDI, 2023)

Imputation Strategies for Clustering Mixed-Type Data with Missing Values. - Rabea Aschenbruck; Gero Szepannek; Adalbert F. X. Wilhelm (J. Classif., 2023)