9 citations · 16 across the 7 of their papers we have counts for
7 papers
A Central Limit Theorem for the permutation importance measure
Nico Föge, Lena Schmid, Marc Ditzhaus +1
Random Forests have become a widely used tool in machine learning since their introduction in 2001, known for their strong performance in classification and regression tasks. One k…
TREE: Tree Regularization for Efficient Execution
Lena Schmid, Daniel Biebert, Christian Hakert +4
The rise of machine learning methods on heavily resource constrained devices requires not only the choice of a suitable model architecture for the target platform, but also the opt…
Even naive trees are consistent
Nico Föge, Markus Pauly, Lena Schmid +1
The last decade has shed some light on theoretical properties such as their consistency for regression tasks. In the current paper, we propose a new class of very simple learners b…
Comparing statistical and machine learning methods for time series forecasting in data-driven logistics -- A simulation study
Lena Schmid, Moritz Roidl, Markus Pauly
Many planning and decision activities in logistics and supply chain management are based on forecasts of multiple time dependent factors. Therefore, the quality of planning depends…
Dataset Bias in Human Activity Recognition
Nilah Ravi Nair, Lena Schmid, Fernando Moya Rueda +3
When creating multi-channel time-series datasets for Human Activity Recognition (HAR), researchers are faced with the issue of subject selection criteria. It is unknown what physic…
Machine Learning for Multi-Output Regression: When should a holistic multivariate approach be preferred over separate univariate ones?
Lena Schmid, Alexander Gerharz, Andreas Groll +1
Tree-based ensembles such as the Random Forest are modern classics among statistical learning methods. In particular, they are used for predicting univariate responses. In case of…
pRSL: Interpretable Multi-label Stacking by Learning Probabilistic Rules
Michael Kirchhof, Lena Schmid, Christopher Reining +2
A key task in multi-label classification is modeling the structure between the involved classes. Modeling this structure by probabilistic and interpretable means enables applicatio…