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F. Markowetz, R. Spang
Molecular Diagnosis. Classification, Model Selection and Performance Evaluation
Keywords: Microarrays, statistical classification, generalization error, model assessment, gene selection
OBJECTIVES: We discuss supervised classification techniques applied to medical diagnosis based on gene expression profiles. Our focus lies on strategies of adaptive model selection to avoid overfitting in highdimensional spaces. METHODS: We introduce likelihood-based methods, classification trees, support vector machines and regularized binary regression. For regularization by dimension reduction, we describe feature selection methods: feature filtering, feature shrinkage and wrapper approaches. In small sample-size situations efficient methods of data re-use are needed to assess the predictive power of a model. We discuss two issues in using cross-validation: the difference between in-loop and out-of-loop feature selection, and estimating model parameters in nested-loop cross-validation. RESULTS: Gene selection does not reduce the dimensionality of the model. Tuning parameters enable adaptive model selection. The feature selection bias is a common pitfall in performance evaluation. Model selection and performance evaluation can be combined by nested-loop cross-validation. CONCLUSIONS: Classification of microarrays is prone to overfitting. A rigorous and unbiased assessment of the predictive power of the model is a must.
Methods of Information in Medicine, Schattauer
Print ISSN: 0026-1270
Volume: 44, 01/2005
Pages: 438 - 443