Abstract
BackgroundFeature 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.ResultsThe 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.ConclusionEFS 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.AvailabilityEFS can be downloaded as an R-package from CRAN or used via a web application at http://EFS.heiderlab.de.
Highlights
Feature selection methods aim at identifying a subset of features that improve the prediction performance of subsequent classification models and thereby simplify their interpretability
Availability: ensemble feature selection (EFS) can be downloaded as an R-package from Comprehensive R Archive Network (CRAN) or used via a web application at http://EFS.heiderlab.de
In the current paper we introduce an R-package and a web server based on the EFS method
Summary
The dataset SPECTF has been obtained from the UCI Machine Learning Repository [20] and is used as an example. The class-variable represents normal (= 0) and abnormal (= 1) results and can be found in the first column of the table of the file SPECTF.csv at the UCI repository. # Create a ROC Curve based on the output from efs eval_tests
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