Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional prediction
about
Multiple-rule bias in the comparison of classification rulesComparison and evaluation of pathway-level aggregation methods of gene expression data.Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation.Stepwise classification of cancer samples using clinical and molecular data.Reverse engineering biomolecular systems using -omic data: challenges, progress and opportunities.Bias correction for selecting the minimal-error classifier from many machine learning modelsA novel hybrid classification model of genetic algorithms, modified k-Nearest Neighbor and developed backpropagation neural network.Added predictive value of omics data: specific issues related to validation illustrated by two case studies.Utilization of never-medicated bipolar disorder patients towards development and validation of a peripheral biomarker profile.Correcting the optimal resampling-based error rate by estimating the error rate of wrapper algorithms.An empirical assessment of validation practices for molecular classifiersThe illusion of distribution-free small-sample classification in genomics.High-dimensional bolstered error estimationA measure of the impact of CV incompleteness on prediction error estimation with application to PCA and normalization.Gram-negative and -positive bacteria differentiation in blood culture samples by headspace volatile compound analysisMethodological issues in current practice may lead to bias in the development of biomarker combinations for predicting acute kidney injury.Molecular differences between chronic and aggressive periodontitisApplication of pattern recognition tools for classifying acute coronary syndrome: an integrated medical modeling.A roadmap for successful applications of clinical proteomics.Exploring Genome-Wide Expression Profiles Using Machine Learning Techniques.Deducing hybrid performance from parental metabolic profiles of young primary roots of maize by using a multivariate diallel approachMachine learning versus statistical modeling.Validation of gene regulatory networks: scientific and inferential.Bootstrapping the out-of-sample predictions for efficient and accurate cross-validation
P2860
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P2860
Optimal classifier selection and negative bias in error rate estimation: an empirical study on high-dimensional prediction
description
2009 nî lūn-bûn
@nan
2009 թուականին հրատարակուած գիտական յօդուած
@hyw
2009 թվականին հրատարակված գիտական հոդված
@hy
2009年の論文
@ja
2009年論文
@yue
2009年論文
@zh-hant
2009年論文
@zh-hk
2009年論文
@zh-mo
2009年論文
@zh-tw
2009年论文
@wuu
name
Optimal classifier selection a ...... on high-dimensional prediction
@ast
Optimal classifier selection a ...... on high-dimensional prediction
@en
Optimal classifier selection a ...... on high-dimensional prediction
@en-gb
Optimal classifier selection a ...... on high-dimensional prediction
@nl
type
label
Optimal classifier selection a ...... on high-dimensional prediction
@ast
Optimal classifier selection a ...... on high-dimensional prediction
@en
Optimal classifier selection a ...... on high-dimensional prediction
@en-gb
Optimal classifier selection a ...... on high-dimensional prediction
@nl
prefLabel
Optimal classifier selection a ...... on high-dimensional prediction
@ast
Optimal classifier selection a ...... on high-dimensional prediction
@en
Optimal classifier selection a ...... on high-dimensional prediction
@en-gb
Optimal classifier selection a ...... on high-dimensional prediction
@nl
P2860
P356
P1476
Optimal classifier selection a ...... on high-dimensional prediction
@en
P2860
P2888
P356
10.1186/1471-2288-9-85
P407
P577
2009-01-01T00:00:00Z
P5875
P6179
1030193087