Classifier variability: accounting for training and testing

DOI10.1016/j.patcog.2011.12.024zbMath1236.68199OpenAlexW1987024366MaRDI QIDQ411930

Waleed A. Yousef, Brandon D. Gallas, Wei-Jie Chen

Publication date: 2 May 2012

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

Full work available at URL: https://doi.org/10.1016/j.patcog.2011.12.024

zbMATH Keywords

AUCU-statisticsclassifier evaluationclassifier stabilitytraining variability

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Blocked 3×2 Cross-Validated t-Test for Comparing Supervised Classification Learning Algorithms, Assessing classifiers in terms of the partial area under the ROC curve

Uses Software

• UCI-ml

Cites Work

• A Framework for Random-Effects ROC Analysis: Biases with the Bootstrap and Other Variance Estimators
• High-dimensional variable selection
• The area above the ordinal dominance graph and the area below the receiver operating characteristic graph
• High-dimensional graphs and variable selection with the Lasso
• Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach
• Improvements on Cross-Validation: The .632+ Bootstrap Method
• Stability Selection
• 10.1162/153244303322753616
• Hypothesis testing of diagnostic accuracy for multiple readers and multiple tests an anova approach with dependent observations
• A Class of Statistics with Asymptotically Normal Distribution
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item