ArticleBioData mining2016
Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approach.
Article in BioData mining, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
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- MoSBi: Automated signature mining for molecular stratification and subtyping.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Diagnostic gene signatures and aberrant pathway activation based on m6A methylation regulators in rheumatoid arthritis.Frontiers in immunology · 2022Article
- Gaussian noise up-sampling is better suited than SMOTE and ADASYN for clinical decision making.BioData mining · 2021Article
- Machine learning with asymmetric abstention for biomedical decision-making.BMC medical informatics and decision making · 2021Article
- Probability calibration-based prediction of recurrence rate in patients with diffuse large B-cell lymphoma.BioData mining · 2021Article
- CpACpP:ACS omega · 2021Article
- Recurrent neural network-based acute concussion classifier using raw resting state EEG data.Scientific reports · 2021Article
- Auto-HMM-LMF: feature selection based method for prediction of drug response via autoencoder and hidden Markov model.BMC bioinformatics · 2021Article
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7 authors.
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No grant is acknowledged in the PubMed record.
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.
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