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Mary Ann Liebert, Journal of Computational Biology, 2(10), p. 119-142, 2003

DOI: 10.1089/106652703321825928

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Estimating Dataset Size Requirements for Classifying DNA Microarray Data

Journal article published in 2003 by Sayan Mukherjee, Pablo Tamayo, Simon Rogers ORCID, Ryan Rifkin, Anna Engle, Colin Campbell, Todd R. Golub, Jill P. Mesirov
This paper is available in a repository.
This paper is available in a repository.

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Abstract

A statistical methodology for estimating dataset size requirements for classifying microarray data using learning curves is introduced. The goal is to use existing classification results to estimate dataset size requirements for future classification experiments and to evaluate the gain in accuracy and significance of classifiers built with additional data. The method is based on fitting inverse power-law models to construct empirical learning curves. It also includes a permutation test procedure to assess the statistical significance of classification performance for a given dataset size. This procedure is applied to several molecular classification problems representing a broad spectrum of levels of complexity.