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Bidirectional Floating Feature Selection Guided by Uncertainty Quantification

Authors: Marcos López De Castro, Farnoosh Abbas-Aghababazadeh, Kewei Ni, Ruben Armañanzas Arnedillo

Contact: mlopezdecas@unav.es, farnoosh.abbasaghababazadeh@uhn.ca, kewei.ni@uhn.ca, rarmananzas@unav.es

Description: Conformal prediction–driven feature selection, with applications in immuno-oncology datasets. We propose a novel bidirectional floating algorithm for feature selection named Conformal Bidirectional Floating Search Algorithm (CBFS), in which the feature search is enhanced by information from the conformal prediction framework.


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Feature selection to find an optimal subset of features

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