22 citations · 51 across the 11 of their papers we have counts for
11 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…
RODD: Robust Outlier Detection in Data Cubes
Lara Kuhlmann, Daniel Wilmes, Emmanuel Müller +2
Data cubes are multidimensional databases, often built from several separate databases, that serve as flexible basis for data analysis. Surprisingly, outlier detection on data cube…
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…
Learning Causal Graphs in Manufacturing Domains using Structural Equation Models
Maximilian Kertel, Stefan Harmeling, Markus Pauly
Many production processes are characterized by numerous and complex cause-and-effect relationships. Since they are only partially known they pose a challenge to effective process c…
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…
On the Relation between Prediction and Imputation Accuracy under Missing Covariates
Burim Ramosaj, Justus Tulowietzki, Markus Pauly
Missing covariates in regression or classification problems can prohibit the direct use of advanced tools for further analysis. Recent research has realized an increasing trend tow…
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…
Travel Time Prediction using Tree-Based Ensembles
He Huang, Martin Pouls, Anne Meyer +1
In this paper, we consider the task of predicting travel times between two arbitrary points in an urban scenario. We view this problem from two temporal perspectives: long-term for…