Back to Search
Start Over
Evaluation of variable selection methods for random forests and omics data sets
- Source :
- Briefings in Bioinformatics
- Publication Year :
- 2017
- Publisher :
- Oxford University Press (OUP), 2017.
-
Abstract
- Machine learning methods and in particular random forests are promising approaches for prediction based on high dimensional omics data sets. They provide variable importance measures to rank predictors according to their predictive power. If building a prediction model is the main goal of a study, often a minimal set of variables with good prediction performance is selected. However, if the objective is the identification of involved variables to find active networks and pathways, approaches that aim to select all relevant variables should be preferred. We evaluated several variable selection procedures based on simulated data as well as publicly available experimental methylation and gene expression data. Our comparison included the Boruta algorithm, the Vita method, recurrent relative variable importance, a permutation approach and its parametric variant (Altmann) as well as recursive feature elimination (RFE). In our simulation studies, Boruta was the most powerful approach, followed closely by the Vita method. Both approaches demonstrated similar stability in variable selection, while Vita was the most robust approach under a pure null model without any predictor variables related to the outcome. In the analysis of the different experimental data sets, Vita demonstrated slightly better stability in variable selection and was less computationally intensive than Boruta. In conclusion, we recommend the Boruta and Vita approaches for the analysis of high-dimensional data sets. Vita is considerably faster than Boruta and thus more suitable for large data sets, but only Boruta can also be applied in low-dimensional settings.
- Subjects :
- Paper
Clustering high-dimensional data
Computer science
0206 medical engineering
high dimensional data
Stability (learning theory)
Breast Neoplasms
Feature selection
02 engineering and technology
computer.software_genre
Machine Learning
Set (abstract data type)
03 medical and health sciences
feature selection
relevant variables
Biomarkers, Tumor
Feature (machine learning)
Humans
Computer Simulation
Molecular Biology
030304 developmental biology
Parametric statistics
0303 health sciences
Gene Expression Profiling
Computational Biology
DNA Methylation
Random forest
Variable (computer science)
Female
Data mining
computer
random forest
Algorithms
020602 bioinformatics
Information Systems
Subjects
Details
- ISSN :
- 14774054
- Volume :
- 20
- Database :
- OpenAIRE
- Journal :
- Briefings in Bioinformatics
- Accession number :
- edsair.doi.dedup.....44f950dee53ffd728c5ab479ba8cfd82