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 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
Наименование:

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

издательство Книга по Требованию Математический анализ. Кратные и криволинейные интегралы. Задачи, решения
Cтраниц 248        мягкая обложка       
Аннотация: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. 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. DNA microarray technology has greatly influenced the realms of biomedical research, with the hopes of significantly impacting the diagnosis and treatment of diseases. 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. These analyses should provide insights into univariate vs. multivariate FSS and how to obtain realistic and honest misclassification error rates via cross-validation (CV).
Издательство: Книга по Требованию
Год:2008
Цена:
3233 руб
Нет в наличии
Поиск: 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
НОВЫЙ ГОД СИМВОЛ 2013 ГОДАУКРАШЕНИЯ
КАРНАВАЛ НА НОВЫЙ ГОД РАСПРОДАЖА
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