Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approachReportar como inadecuado




Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approach - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

BioData Mining

, 9:36

First Online: 18 November 2016Received: 23 June 2016Accepted: 27 October 2016

Abstract

MotivationBiomarker 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.

ResultsBy 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.

KeywordsMachine learning Feature selection Ensemble learning Biomarker discovery Random forest AbbrevationsAUCArea under the curve

CARTClassification and regression tree

CPUCentral processing unit

EFSEnsemble feature selection

FSFeature selection

LOOCVLeave-one-out cross validation

OOBOut-of-bag

RFRandom forest

ROCReceiver operating characteristic

VIMVariable importance measure

Electronic supplementary materialThe online version of this article doi:10.1186-s13040-016-0114-4 contains supplementary material, which is available to authorized users.

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Autor: Ursula Neumann - Mona Riemenschneider - Jan-Peter Sowa - Theodor Baars - Julia Kälsch - Ali Canbay - Dominik Heider

Fuente: https://link.springer.com/



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