13 citations · 44 across the 11 of their papers we have counts for
12 papers
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…
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…
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…
Improving Adaptive Seamless Designs through Bayesian optimization
Jakob Richter, Tim Friede, Jörg Rahnenführer
We propose to use Bayesian optimization (BO) to improve the efficiency of the design selection process in clinical trials. BO is a method to optimize expensive black-box functions,…
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…
Feature Selection Methods for Cost-Constrained Classification in Random Forests
Rudolf Jagdhuber, Michel Lang, Jörg Rahnenführer
Cost-sensitive feature selection describes a feature selection problem, where features raise individual costs for inclusion in a model. These costs allow to incorporate disfavored…
Implications on Feature Detection when using the Benefit-Cost Ratio
Rudolf Jagdhuber, Jörg Rahnenführer
In many practical machine learning applications, there are two objectives: one is to maximize predictive accuracy and the other is to minimize costs of the resulting model. These c…
Combining heterogeneous subgroups with graph-structured variable selection priors for Cox regression
Katrin Madjar, Manuela Zucknick, Katja Ickstadt +1
Important objectives in cancer research are the prediction of a patient's risk based on molecular measurements such as gene expression data and the identification of new prognostic…
Weighted Cox regression for the prediction of heterogeneous patient subgroups
Katrin Madjar, Jörg Rahnenführer
An important task in clinical medicine is the construction of risk prediction models for specific subgroups of patients based on high-dimensional molecular measurements such as gen…
Improving Reliability of Latent Dirichlet Allocation by Assessing Its Stability Using Clustering Techniques on Replicated Runs
Jonas Rieger, Lars Koppers, Carsten Jentsch +1
For organizing large text corpora topic modeling provides useful tools. A widely used method is Latent Dirichlet Allocation (LDA), a generative probabilistic model which models sin…
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…
TiMEx: A Waiting Time Model for Mutually Exclusive Groups of Cancer Alterations
Simona Constantinescu, Ewa Szczurek, Pejman Mohammadi +2
Despite recent technological advances in genomic sciences, our understanding of cancer progression and its driving genetic alterations remains incomplete. Here, we introduce TiMEx,…