Search Results for author: Lukas Pfannschmidt

Found 4 papers, 2 papers with code

Sequential Feature Classification in the Context of Redundancies

3 code implementations1 Apr 2020 Lukas Pfannschmidt, Barbara Hammer

The problem of all-relevant feature selection is concerned with finding a relevant feature set with preserved redundancies.

Classification General Classification

Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information

no code implementations10 Dec 2019 Lukas Pfannschmidt, Jonathan Jakob, Fabian Hinder, Michael Biehl, Peter Tino, Barbara Hammer

In this contribution, we focus on feature selection paradigms, which enable us to uncover relevant factors of a given regularity based on a sparse model.


FRI -- Feature Relevance Intervals for Interpretable and Interactive Data Exploration

no code implementations2 Mar 2019 Lukas Pfannschmidt, Christina Göpfert, Ursula Neumann, Dominik Heider, Barbara Hammer

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 selected due to spurious effects or correlations rather than causal effects.

General Classification regression

Feature Relevance Bounds for Ordinal Regression

1 code implementation20 Feb 2019 Lukas Pfannschmidt, Jonathan Jakob, Michael Biehl, Peter Tino, Barbara Hammer

The increasing occurrence of ordinal data, mainly sociodemographic, led to a renewed research interest in ordinal regression, i. e. the prediction of ordered classes.


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