Evaluating Microarray-based Classifiers: An Overview
Author(s) -
AnneLaure Boulesteix,
Carolin Strobl,
Thomas Augustin,
Martin Däumer
Publication year - 2008
Publication title -
cancer informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.606
H-Index - 31
ISSN - 1176-9351
DOI - 10.4137/cin.s408
Subject(s) - computer science , classifier (uml) , machine learning , feature selection , data mining , artificial intelligence , data science
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.
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