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Dalia Rodriguez Salas M.Eng.

Researcher in the Learning Approaches for Medical Big Data Analysis (LAMBDA) group at the Pattern Recognition Lab of the Friedrich-Alexander-Universität Erlangen-Nürnberg

Feature selection for regression problems with high correlated features

Feature selection for regression problems can be highly beneficial in terms of robustness and execution speed.

Most common filter algorithms for feature selection are focused on the interactions of the features with the output variable, giving diminished attention to the interactions among features. Therefore, their selected subset of features does not consider features that by themselves are useless but when taken with others, they provide a significant performance improvement on the regression algorithm. The aim of this project is to consider both kinds of interactions in order to obtain a more robust feature subset.

Heterogeneous Image Systems

I am a member of the research training group "Heterogeneous Image Systems". For more information, please click here.