The effect of noisy bootstrapping on the robustness of supervised classification of gene expression data

Niv Efron, Nathan Intrator

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper discusses the role of noisy bootstrapping in the analysis of microarray data. We apply linear discriminant analysis, according to Fisher's method, to perform feature selection and classification, creating a linear model which enables clinicians easier interpretation of the results. We present the effects of bootstrapping in improvement of the results, and specifically robustifying classification with an increased number of genes. The performance of our method is demonstrated on publicly available datasets, and a comparison with state of the art published results is included. In particular, we show the effect of the number of features (genes) on the result, as well as the effect of bootstrapping. The results show that our classifier is accurate and quite competitive to other classifiers, although it is simpler, and enables considering a larger set of genes in the classification.

Original languageEnglish
Title of host publicationMachine Learning for Signal Processing XIV - Proceedings of 2004 IEEE Signal Processing Society Workshop
EditorsA. Barros, J. Principe, J. Larsen, T. Adali, S. Douglas
Pages413-422
Number of pages10
StatePublished - 2004
EventMachine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop - Sao Luis, Brazil
Duration: 29 Sep 20041 Oct 2004

Publication series

NameMachine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop

Conference

ConferenceMachine Learning for Signal Processing XIV - Proceedings of the 2004 IEEE Signal Processing Society Workshop
Country/TerritoryBrazil
CitySao Luis
Period29/09/041/10/04

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