Paper accepted!

BioDataMining

Our paper has been accepted in BioData Mining:

Neumann U, Genze N, Heider D: EFS: An Ensemble Feature Selection Tool implemented as R-package and Web-Application. BioData Mining 2017, 10:21. (Link)

Abstract

Background
Feature selection methods aim at identifying a subset of features that improve the prediction performance of subsequent classification models and thereby also simplify their interpretability. Preceding studies demonstrated that single feature selection methods can have specific biases, whereas an ensemble feature selection has the advantage to alleviate and compensate for these biases.

Results
The software EFS (Ensemble Feature Selection) makes use of multiple feature selection methods and combines their normalized outputs to a quantitative ensemble importance. Currently, eight different feature selection methods have been integrated in EFS, which can be used separately or combined in an ensemble.

Conclusion
EFS identifies relevant features while compensating specific biases of single methods due to an ensemble approach. Thereby, EFS can improve the prediction accuracy and interpretability in subsequent binary classification models.

Availability
EFS can be downloaded as an R-package from CRAN or used via a web application at http://EFS.heiderlab.de

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Written by: Heider