5 citations · 8 across the 4 of their papers we have counts for
4 papers
Sequential Feature Classification in the Context of Redundancies
Lukas Pfannschmidt, Barbara Hammer
The problem of all-relevant feature selection is concerned with finding a relevant feature set with preserved redundancies. There exist several approximations to solve this problem…
Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information
Lukas Pfannschmidt, Jonathan Jakob, Fabian Hinder +3
Advances in machine learning technologies have led to increasingly powerful models in particular in the context of big data. Yet, many application scenarios demand for robustly int…
FRI -- Feature Relevance Intervals for Interpretable and Interactive Data Exploration
Lukas Pfannschmidt, Christina Göpfert, Ursula Neumann +2
Most existing feature selection methods are insufficient for analytic purposes as soon as high dimensional data or redundant sensor signals are dealt with since features can be sel…
Feature Relevance Bounds for Ordinal Regression
Lukas Pfannschmidt, Jonathan Jakob, Michael Biehl +2
The increasing occurrence of ordinal data, mainly sociodemographic, led to a renewed research interest in ordinal regression, i.e. the prediction of ordered classes. Besides model…