Optimal number of features as a function of sample size for various classification rules.
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Performance reproducibility index for classificationGene selection and classification of microarray data using random forest.Comparative study of classification algorithms for immunosignaturing dataConvergence of biomarkers, bioinformatics and nanotechnology for individualized cancer treatmentMCMC implementation of the optimal Bayesian classifier for non-Gaussian models: model-based RNA-Seq classification.A comparison of machine learning methods for classification using simulation with multiple real data examples from mental health studiesNoise-injected neural networks show promise for use on small-sample expression data.Three methods for optimization of cross-laboratory and cross-platform microarray expression data.Gene selection for classification of microarray data based on the Bayes error.Classification with reject option in gene expression data.Selecting normalization genes for small diagnostic microarrays.Automated diagnosis of fetal alcohol syndrome using 3D facial image analysisA multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data.Classification and error estimation for discrete data.Multi-factorial analysis of class prediction error: estimating optimal number of biomarkers for various classification rules.A hybrid BPSO-CGA approach for gene selection and classification of microarray data.The Model-Based Study of the Effectiveness of Reporting Lists of Small Feature Sets Using RNA-Seq DataBiomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset.Abnormal changes of multidimensional surface features using multivariate pattern classification in amnestic mild cognitive impairment patientsA new method for identifying bivariate differential expression in high dimensional microarray data using quadratic discriminant analysis.Predicting antitumor activity of peptides by consensus of regression models trained on a small data sample.Classification and lateralization of temporal lobe epilepsies with and without hippocampal atrophy based on whole-brain automatic MRI segmentation.Modeling pathologic response of esophageal cancer to chemoradiation therapy using spatial-temporal 18F-FDG PET features, clinical parameters, and demographicsEffect of separate sampling on classification accuracy.Normalization benefits microarray-based classification.Lung texture in serial thoracic computed tomography scans: correlation of radiomics-based features with radiation therapy dose and radiation pneumonitis development.High-dimensional bolstered error estimationStudy design requirements for RNA sequencing-based breast cancer diagnosticsDNA methylation loci associated with atopy and high serum IgE: a genome-wide application of recursive Random Forest feature selection.Risk estimation and risk prediction using machine-learning methods.Quantitative classification of pediatric swallowing through accelerometry.Incorporation of pre-therapy 18 F-FDG uptake data with CT texture features into a radiomics model for radiation pneumonitis diagnosis.A multi-gene approach to differentiate papillary thyroid carcinoma from benign lesions: gene selection using support vector machines with bootstrappingUsing the information embedded in the testing sample to break the limits caused by the small sample size in microarray-based classification.Validation of computational methods in genomics.Fusing Gene Interaction to Improve Disease Discrimination on Classification Analysis.Performance of feature selection methods.Classifying Glioblastoma Multiforme Follow-Up Progressive vs. Responsive Forms Using Multi-Parametric MRI Features.Gene expression profiling for targeted cancer treatment.Decorrelation of the true and estimated classifier errors in high-dimensional settings.
P2860
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P2860
Optimal number of features as a function of sample size for various classification rules.
description
2004 nî lūn-bûn
@nan
2004年の論文
@ja
2004年学术文章
@wuu
2004年学术文章
@zh
2004年学术文章
@zh-cn
2004年学术文章
@zh-hans
2004年学术文章
@zh-my
2004年学术文章
@zh-sg
2004年學術文章
@yue
2004年學術文章
@zh-hant
name
Optimal number of features as a function of sample size for various classification rules.
@en
Optimal number of features as a function of sample size for various classification rules.
@nl
type
label
Optimal number of features as a function of sample size for various classification rules.
@en
Optimal number of features as a function of sample size for various classification rules.
@nl
prefLabel
Optimal number of features as a function of sample size for various classification rules.
@en
Optimal number of features as a function of sample size for various classification rules.
@nl
P2093
P2860
P356
P1433
P1476
Optimal number of features as a function of sample size for various classification rules.
@en
P2093
Edward Suh
James Lowey
Jianping Hua
Zixiang Xiong
P2860
P304
P356
10.1093/BIOINFORMATICS/BTI171
P407
P577
2004-11-30T00:00:00Z