activity
20192026
most citedAdjusted Measures for Feature Selection Stability for Data Sets with Similar Features

13 citations · 35 across the 6 of their papers we have counts for

collaborators

6 papers

stat.ME2026

An Empirical Comparison of Methods for Quantifying the Similarity of Numeric Datasets

Marieke Stolte, Jörg Rahnenführer, Andrea Bommert

Methods for quantifying the similarity of datasets are relevant in applications where two or more datasets, or their underlying distributions, need to be compared, ranging from two…

stat.ME2023★ 11 cited

Methods for Quantifying Dataset Similarity: a Review, Taxonomy and Comparison

Marieke Stolte, Franziska Kappenberg, Jörg Rahnenführer +1

Quantifying the similarity between datasets has widespread applications in statistics and machine learning. The performance of a predictive model on novel datasets, referred to as…

stat.ME2023★ 5 cited

Simulation study to evaluate when Plasmode simulation is superior to parametric simulation in estimating the mean squared error of the least squares estimator in linear regression

Marieke Stolte, Nicholas Schreck, Alla Slynko +4

Simulation is a crucial tool for the evaluation and comparison of statistical methods. How to design fair and neutral simulation studies is therefore of great interest for research…

stat.ML2021★ 3 cited

Employing an Adjusted Stability Measure for Multi-Criteria Model Fitting on Data Sets with Similar Features

Andrea Bommert, Jörg Rahnenführer, Michel Lang

Fitting models with high predictive accuracy that include all relevant but no irrelevant or redundant features is a challenging task on data sets with similar (e.g. highly correlat…

stat.ML2020★ 13 cited

Adjusted Measures for Feature Selection Stability for Data Sets with Similar Features

Andrea Bommert, Jörg Rahnenführer

For data sets with similar features, for example highly correlated features, most existing stability measures behave in an undesired way: They consider features that are almost ide…

cs.LG2019★ 3 cited

High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions

Xudong Sun, Andrea Bommert, Florian Pfisterer +3

A novel machine learning optimization process coined Restrictive Federated Model Selection (RFMS) is proposed under the scenario, for example, when data from healthcare units can n…