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Improving model robustness with bootstrapping -- application to optimal discriminant analysis for ordinal responses
G Le Teuff1, C Quantin, A Venot
1Service de Biostatistique et Informatique Médicale, Centre Hospitalier Universitaire, 1 boulevard Jeanne d'Arc-BP 77908, 21079 Dijon Cedex, France.
Methods of Information in Medicine
|January 10, 2006
Summary
Optimal discriminating analysis for ordinal responses (ODAO) can be sensitive to data sampling. This study introduces bootstrapping methods to enhance ODAO model stability, particularly for small datasets, improving classification robustness.
Area of Science:
- Statistical analysis
- Machine learning
- Biostatistics
Background:
- Optimal discriminating analysis for ordinal responses (ODAO) shows promise but can be sensitive to sampling variability.
- Classic methods like Fisher's discrimination and logistic regression have limitations with ordinal data.
Purpose of the Study:
- To develop methods for controlling sampling fluctuations and ensuring model stability in ODAO.
- To improve the robustness of ODAO models, especially with limited training data.
Main Methods:
- Intensive computational methods and bootstrapping were employed during model building.
- Coefficient estimation minimized an aggregate criterion of bootstrapped replications, rather than solely the training sample criterion.
- Five aggregate criteria were investigated for their effectiveness.
Main Results:
- Robustness improvements were observed in 30% of test cases with moderate training sample sizes.
- Significant robustness gains were noted in 55% of test cases with small training sample sizes.
Conclusions:
- Bootstrapping effectively enhances the robustness of ODAO models.
- These methods are particularly beneficial in challenging classification scenarios and with small, frequently encountered training sample sizes.
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