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Componentes principales supervisados...

Porras Cerron, Jaime Carlos.

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Componentes principales supervisados para clasificacion de datos de expresion genetica (Spanish text).

Record Type:	Electronic resources : Monograph/item
Title/Author:	Componentes principales supervisados para clasificacion de datos de expresion genetica (Spanish text)./
Author:	Porras Cerron, Jaime Carlos.
Description:	115 p.
Notes:	Source: Masters Abstracts International, Volume: 44-03, page: 1390.
Contained By:	Masters Abstracts International44-03.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1431351
ISBN:	9780542459672

Componentes principales supervisados para clasificacion de datos de expresion genetica (Spanish text).
Porras Cerron, Jaime Carlos.

Componentes principales supervisados para clasificacion de datos de expresion genetica (Spanish text). - 115 p.

Source: Masters Abstracts International, Volume: 44-03, page: 1390.

Thesis (M.S.)--University of Puerto Rico, Mayaguez (Puerto Rico), 2006.

The gene expression data obtained through the technology of microarrays are characterized by its considerably greater amount of features in comparison to the number of observations. The direct use of traditional statistics techniques of supervised classification can give poor results in gene expression data. Therefore before analyzing this type of data is advisable to perform a dimension reduction. The present work combines two types of dimensional reduction techniques: feature selection and feature extraction. In the first step of the proposed method feature selection is applied, and in the second step principal components are formed with the selected features. This technique is called Supervised Principal Components (SPC). Three classifiers are applied to these components and the misclassification error is estimated. Two algorithms of SPC are presented; they essentially, differ in the time in which the feature selection is made. Finally, the results of this new technique are applied to nine gene expression data sets.

ISBN: 9780542459672Subjects--Topical Terms:

517247
Statistics.

Componentes principales supervisados para clasificacion de datos de expresion genetica (Spanish text).
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100 1 $a Porras Cerron, Jaime Carlos. $3 1914492
245 1 0 $a Componentes principales supervisados para clasificacion de datos de expresion genetica (Spanish text).
300 $a 115 p.
500 $a Source: Masters Abstracts International, Volume: 44-03, page: 1390.
500 $a Adviser: Edgar Acuna.
502 $a Thesis (M.S.)--University of Puerto Rico, Mayaguez (Puerto Rico), 2006.
520 $a The gene expression data obtained through the technology of microarrays are characterized by its considerably greater amount of features in comparison to the number of observations. The direct use of traditional statistics techniques of supervised classification can give poor results in gene expression data. Therefore before analyzing this type of data is advisable to perform a dimension reduction. The present work combines two types of dimensional reduction techniques: feature selection and feature extraction. In the first step of the proposed method feature selection is applied, and in the second step principal components are formed with the selected features. This technique is called Supervised Principal Components (SPC). Three classifiers are applied to these components and the misclassification error is estimated. Two algorithms of SPC are presented; they essentially, differ in the time in which the feature selection is made. Finally, the results of this new technique are applied to nine gene expression data sets.
590 $a School code: 0553.
650 4 $a Statistics. $3 517247
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Computer Science. $3 626642
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710 2 0 $a University of Puerto Rico, Mayaguez (Puerto Rico). $3 1017811
773 0 $t Masters Abstracts International $g 44-03.
790 1 0 $a Acuna, Edgar, $e advisor
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793 $a Spanish
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1431351