title: Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approach.
abstract: BACKGROUND
  Biomarker discovery methods are essential to identify a minimal subset of features (e.g., serum markers in predictive medicine) that are relevant to develop prediction models with high accuracy. By now, there exist diverse feature selection methods, which either are embedded, combined, or independent of predictive learning algorithms. Many preceding studies showed the defectiveness of single feature selection results, which cause difficulties for professionals in a variety of fields (e.g., medical practitioners) to analyze and interpret the obtained feature subsets. Whereas each of these methods is highly biased, an ensemble feature selection has the advantage to alleviate and compensate for such biases. Concerning the reliability, validity, and reproducibility of these methods, we examined eight different feature selection methods for binary classification datasets and developed an ensemble feature selection system.
  
  
  RESULTS
  By using an ensemble of feature selection methods, a quantification of the importance of the features could be obtained. The prediction models that have been trained on the selected features showed improved prediction performance.
authors.affiliation: Department of Bioinformatics, Straubing, 94315 Germany ; University of Applied Science, Weihenstephan-Triesdorf, Freising, 85354 Germany ; Wissenschaftszentrum Weihenstephan, Technische Universität München, Freising, 85354 Germany.
authors.forename: Ursula
authors.initials: U
authors.lastname: Neumann
authors.affiliation: Department of Bioinformatics, Straubing, 94315 Germany ; University of Applied Science, Weihenstephan-Triesdorf, Freising, 85354 Germany.
authors.forename: Mona
authors.initials: M
authors.lastname: Riemenschneider
authors.affiliation: Department of Gastroenterology and Hepatology, University Hospital, University Duisburg-Essen, Essen, 45122 Germany.
authors.forename: Jan-Peter
authors.initials: JP
authors.lastname: Sowa
authors.affiliation: Clinic for Cardiology, West German Heart and Vascular Centre Essen, University Hospital, University Duisburg-Essen, Essen, 45122 Germany.
authors.forename: Theodor
authors.initials: T
authors.lastname: Baars
authors.affiliation: Department of Gastroenterology and Hepatology, University Hospital, University Duisburg-Essen, Essen, 45122 Germany.
authors.forename: Julia
authors.initials: J
authors.lastname: Kälsch
authors.affiliation: Department of Gastroenterology and Hepatology, University Hospital, University Duisburg-Essen, Essen, 45122 Germany.
authors.forename: Ali
authors.initials: A
authors.lastname: Canbay
authors.affiliation: Department of Bioinformatics, Straubing, 94315 Germany ; University of Applied Science, Weihenstephan-Triesdorf, Freising, 85354 Germany ; Wissenschaftszentrum Weihenstephan, Technische Universität München, Freising, 85354 Germany.
authors.forename: Dominik
authors.initials: D
authors.lastname: Heider
keywords: Biomarker discovery; Ensemble learning; Feature selection; Machine learning; Random forest