Selecting features in microarray classification using roc curves

DOI10.1016/J.PATCOG.2006.07.010zbMath1103.68774OpenAlexW2030415119MaRDI QIDQ850144

Hiroshi Mamitsuka

Publication date: 15 November 2006

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2006.07.010

zbMATH Keywords

binary classificationfeature subset selectioncDNA microarrayarea between the ROC curve and the diagonal linearea between the ROC curvesnon-parametric hypothesis testingROC (receiver operating characteristic) curve

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10)

Related Items (5)

ROC curves for regression ⋮ On eigen-matrix translation method for classification of biological data ⋮ Efficient semi-supervised learning on locally informative multiple graphs ⋮ A dynamic over-sampling procedure based on sensitivity for multi-class problems ⋮ D.C. programming for sparse proximal support vector machines

Uses Software

• SVMlight
• HandTill2001
• C4.5
• sma

Cites Work

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• Wrappers for feature subset selection
• Multiple hypothesis testing in microarray experiments.
• 10.1162/153244303322753616
• On Information and Sufficiency
• A simple generalisation of the area under the ROC curve for multiple class classification problems

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