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DOI: 10.1055/s-0038-1633990
Molecular Diagnosis
Classification, Model Selection and Performance EvaluationPublikationsverlauf
Publikationsdatum:
06. Februar 2018 (online)
![](https://www.thieme-connect.de/media/10.1055-s-00035037/200503/lookinside/thumbnails/10-1055-s-0038-1633990-1.jpg)
Summary
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 high-dimensional 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.
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