ArticleBioinformatics (Oxford, England)2011
High-dimensional bolstered error estimation.
Article in Bioinformatics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Article
- Ten quick tips for biomarker discovery and validation analyses using machine learning.PLoS computational biology · 2022Article
- Biomarker discovery studies for patient stratification using machine learning analysis of omics data: a scoping review.BMJ open · 2021Article
- The fecal microbiome in dogs with acute diarrhea and idiopathic inflammatory bowel disease.PloS one · 2012Article
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Authors and funding
3 authors.
Funding
Abstract
motivationIn small-sample settings, bolstered error estimation has been shown to perform better than cross-validation and competitively with bootstrap with regard to various criteria. The key issue for bolstering performance is the variance setting for the bolstering kernel. Heretofore, this variance has been determined in a non-parametric manner from the data. Although bolstering based on this variance setting works well for small feature sets, results can deteriorate for high-dimensional feature spaces.
resultsThis article computes an optimal kernel variance depending on the classification rule, sample size, model and feature space, both the original number and the number remaining after feature selection. A key point is that the optimal variance is robust relative to the model. This allows us to develop a method for selecting a suitable variance to use in real-world applications where the model is not known, but the other factors in determining the optimal kernel are known. AVAILABILITY: Companion website at http://compbio.tgen.org/paper_supp/high_dim_bolstering. CONTACT: edward@mail.ece.tamu.edu.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.