Hybrid Dimensionality Reduction for Classification of Microarray Data - Brossura

Arowolo, Micheal; Gbolagade, Kazeem; Abdulsalam, Sulaiman

 
9786202064330: Hybrid Dimensionality Reduction for Classification of Microarray Data
Brossura
ISBN 10: 6202064331 ISBN 13: 9786202064330
Casa editrice: LAP LAMBERT Academic Publishing, 2017

Sinossi

High dimensionality affects the performance of classifiers, especially for microarray gene expression data sets. Many efficient dimensionality reduction techniques that transform these high dimensional data into a reduced form have been proposed for microarray data analysis. These techniques perform well. However, these techniques need to be improved in systematic ways as regards to their performance metrics. This study combines the two dimensionality reduction technique, feature selection and feature extraction, to address the problems of highly correlated data and selection of significant variables out of a set of features, by assessing important and significant dimensionality reduction techniques contributing to efficient classification of genes in a data. One-Way-ANOVA is employed for feature selection to obtain an optimal number of genes; Principal Component Analysis (PCA) as well as Partial Least Squares (PLS) is employed as feature extraction methods separately, to reduce the selected features from microarray dataset. An experimental result on colon cancer dataset uses Support Vector Machine (SVM) as a classifier.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Editore
LAP LAMBERT Academic Publishing
Data di pubblicazione
2017
Lingua
Inglese
ISBN 10 
6202064331
ISBN 13 
9786202064330
Rilegatura
Copertina flessibile
Numero di pagine
116
Contatto del produttore
non disponibile
Persona responsabile
KreGa Onlinehandel GmbH
4950000000000
info@krega-onlinehandel.de

Birkenweg 31
Brakel
33034
Germania