Malte Nalenz

Statistical Comparisons of Classifiers by Generalized Stochastic Dominance

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 to several...

Comparing Machine Learning Algorithms by Union-Free Generic Depth

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 very little...

Depth Functions for Partial Orders with a Descriptive Analysis of Machine Learning Algorithms

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 spaces, there...

Evaluating machine learning models in non-standard settings: An overview and new findings

Estimating the generalization error (GE) of machine learning models is fundamental, with resampling methods being the most common approach. However, in non-standard settings, particularly those...

Depth functions for partial orders with a descriptive analysis of machine learning algorithms

Compressed Rule Ensemble Learning