A multicriteria approach to find predictive and sparse models with stable feature selection for high-dimensional data

DOI10.1155/2017/7907163zbMath1397.92016OpenAlexW2741799640WikidataQ38611070 ScholiaQ38611070MaRDI QIDQ1664508

Jörg Rahnenführer, Michel Lang, Andrea Bommert

Publication date: 27 August 2018

Published in: Computational \& Mathematical Methods in Medicine (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2017/7907163

zbMATH Keywords

bioinformaticshigh-dimensional datamulticriteria approachpredictive and sparse modelsstable feature selection

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) General biostatistics (92B15) Genetics and epigenetics (92D10)

Related Items (2)

Benchmark for filter methods for feature selection in high-dimensional classification data ⋮ Feature selection method based on partial least squares and analysis of traditional Chinese medicine data

Uses Software

• R
• ROCR
• mboost
• Kernlab
• mlr
• ranger
• BatchJobs
• BatchExperiments
• OpenML
• fmrmr
• LiblineaR

Cites Work

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• Model-based boosting in R: a hands-on tutorial using the R package mboost
• Stable feature selection for biomarker discovery
• Measuring and visualizing the stability of biomarker selection techniques
• Comparing the Characteristics of Gene Expression Profiles Derived by Univariate and Multivariate Classification Methods
• Boosting With theL₂Loss
• Stability Selection
• Automatic model selection for high-dimensional survival analysis

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