2 citations · 2 across the 3 of their papers we have counts for
4 papers
Comparing Machine Learning Algorithms by Union-Free Generic Depth
Hannah Blocher, Georg Schollmeyer, Malte Nalenz +1
We propose a framework for descriptively analyzing sets of partial orders based on the concept of depth functions. Despite intensive studies in linear and metric spaces, there is v…
Evaluating machine learning models in non-standard settings: An overview and new findings
Roman Hornung, Malte Nalenz, Lennart Schneider +5
Estimating the generalization error (GE) of machine learning models is fundamental, with resampling methods being the most common approach. However, in non-standard settings, parti…
Depth Functions for Partial Orders with a Descriptive Analysis of Machine Learning Algorithms
Hannah Blocher, Georg Schollmeyer, Christoph Jansen +1
We propose a framework for descriptively analyzing sets of partial orders based on the concept of depth functions. Despite intensive studies of depth functions in linear and metric…
Statistical Comparisons of Classifiers by Generalized Stochastic Dominance
Christoph Jansen, Malte Nalenz, Georg Schollmeyer +1
Although being a crucial question for the development of machine learning algorithms, there is still no consensus on how to compare classifiers over multiple data sets with respect…