Andere Kunden interessierten sich auch für
![Scalable dimensionality reduction methods for recommender systems]( "Scalable dimensionality reduction methods for recommender systems")
Scalable dimensionality reduction methods for recommender systems57,99 €![DIMENSIONALITY REDUCTION-BASED RECOMMENDATIONS WITH PRIVACY]( "DIMENSIONALITY REDUCTION-BASED RECOMMENDATIONS WITH PRIVACY")
DIMENSIONALITY REDUCTION-BASED RECOMMENDATIONS WITH PRIVACY35,99 €![Novel Approaches to Dimensionality Reduction and Applications]( "Novel Approaches to Dimensionality Reduction and Applications")
Novel Approaches to Dimensionality Reduction and Applications56,99 €![Dimensionality Reduction with Unsupervised Nearest Neighbors]( "Dimensionality Reduction with Unsupervised Nearest Neighbors")
Dimensionality Reduction with Unsupervised Nearest Neighbors74,99 €![LINEAR DATA MINING MODEL WITH VERY FEW MINIMUM GENE FEATURES]( "LINEAR DATA MINING MODEL WITH VERY FEW MINIMUM GENE FEATURES")
LINEAR DATA MINING MODEL WITH VERY FEW MINIMUM GENE FEATURES35,99 €![Open Problems in Spectral Dimensionality Reduction]( "Open Problems in Spectral Dimensionality Reduction")
Open Problems in Spectral Dimensionality Reduction37,99 €![Microarray Image Denoising]( "Microarray Image Denoising")
Microarray Image Denoising57,99 €
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.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.