Evaluating microarray-based classifiers: an overview

Boulesteix, Anne-Laure; Strobl, Carolin; Augustin, Thomas and Daumer, Martin (2007): Evaluating microarray-based classifiers: an overview. Department of Statistics: Technical Reports, No.5

Abstract

For the last eight years, microarray-based class prediction has been the subject of numerous publications in medicine, bioinformatics and statistics journals. However, in many articles, the assessment of classification accuracy is carried out using suboptimal procedures and is not paid much attention. In this paper, we carefully review various statistical aspects of classifier evaluation and validation from a practical point of view. The main topics addressed are accuracy measures, error rate estimation procedures, variable selection, choice of classifiers and validation strategy.

• Item Type: Paper
• Keywords: accuracy measures, classification, conditional and unconditional error rate, error rate estimation, validation data, variable selection
• Faculties: Mathematics, Computer Science and Statistics > Statistics > Technical Reports
• Language: English