Bookcover of Feature Selection and Binary Classification Using Microarray Data
Booktitle:

Feature Selection and Binary Classification Using Microarray Data

Background, Methods, and Comparison of Classifiers Using Univariate and Genetic Algorithm-Based Feature Selection and Supervised Learning Techniques

VDM Verlag Dr. Müller (2008-04-08 )

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ISBN-13:

978-3-8364-7596-9

ISBN-10:
3836475960
EAN:
9783836475969
Book language:
English
Blurb/Shorttext:
DNA microarray technology has greatly influenced the realms of biomedical research, with the hopes of significantly impacting the diagnosis and treatment of diseases. Microarrays have the ability to measure the expression levels of thousands of genes simultaneously. This research entails a rigorous and systematic comparison of statistical classifiers composed of supervised learning methods and either univariate or genetic algorithm-based multivariate feature subset selection (FSS) techniques, all applied to six published two-class microarray datasets. These analyses should provide insights into univariate vs. multivariate FSS and how to obtain realistic and honest misclassification error rates via cross-validation (CV). Ultimately, this research puts to test the more traditional implementations of CV and FSS and provides a solid foundation on which these topics can and should be further investigated when performing limited-sample classification using high-dimensional gene expression data. The analyses presented should be especially useful to researchers in the fields of bioinformatics, supervised learning, and variable selection, as well as anyone interested in microarray analysis.
Publishing house:
VDM Verlag Dr. Müller
Website:
http://www.vdm-verlag.de
By (author) :
Michael Lecocke
Number of pages:
252
Published at:
2008-04-08
Stock:
Available
Category:
Mathematics
Price:
79.00 €
Keywords:
bioinformatics: microarrays: gene expression: supervised learning: binary classification: feature selection: genetic algorithm: cross-validation, Bioinformatics, Microarrays, gene expression, supervised learning, binary classification, Feature selection, genetic algorithm, Cross-Validation

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