FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration

Pfannschmidt L, Göpfert C, Neumann U, Heider D, Hammer B (2019)
Presented at the 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, Certosa di Pontignano, Siena - Tuscany, Italy.

Konferenzbeitrag | Veröffentlicht | Englisch
 
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Abstract / Bemerkung
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. To support the finding of causal features in biomedical experiments, we hereby present FRI, an open source Python library that can be used to identify all-relevant variables in linear classification and (ordinal) regression problems. Using the recently proposed feature relevance method, FRI is able to provide the base for further general experimentation or in specific can facilitate the search for alternative biomarkers. It can be used in an interactive context, by providing model manipulation and visualization methods, or in a batch process as a filter method.
Stichworte
global feature relevance; feature selection; interpretability; interactive biomarker discovery
Erscheinungsjahr
2019
Konferenz
16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology
Konferenzort
Certosa di Pontignano, Siena - Tuscany, Italy
Konferenzdatum
2019-07-09 – 2019-07-11
eISBN
978-1-7281-1462-0
Page URI
https://pub.uni-bielefeld.de/record/2935456

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Pfannschmidt L, Göpfert C, Neumann U, Heider D, Hammer B. FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration. Presented at the 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, Certosa di Pontignano, Siena - Tuscany, Italy.
Pfannschmidt, L., Göpfert, C., Neumann, U., Heider, D., & Hammer, B. (2019). FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration. Presented at the 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, Certosa di Pontignano, Siena - Tuscany, Italy. doi:10.1109/CIBCB.2019.8791489
Pfannschmidt, L., Göpfert, C., Neumann, U., Heider, D., and Hammer, B. (2019).“FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration”. Presented at the 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, Certosa di Pontignano, Siena - Tuscany, Italy.
Pfannschmidt, L., et al., 2019. FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration. Presented at the 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, Certosa di Pontignano, Siena - Tuscany, Italy.
L. Pfannschmidt, et al., “FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration”, Presented at the 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, Certosa di Pontignano, Siena - Tuscany, Italy, 2019.
Pfannschmidt, L., Göpfert, C., Neumann, U., Heider, D., Hammer, B.: FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration. Presented at the 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, Certosa di Pontignano, Siena - Tuscany, Italy (2019).
Pfannschmidt, Lukas, Göpfert, Christina, Neumann, Ursula, Heider, Dominik, and Hammer, Barbara. “FRI - Feature Relevance Intervals for Interpretable and Interactive Data Exploration”. Presented at the 16th IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology, Certosa di Pontignano, Siena - Tuscany, Italy, 2019.
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2019-04-30T17:30:18Z
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arXiv: 1903.00719

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