DocumentCode :
3668024
Title :
Review on feature selection methods in high dimensional domains
Author :
Devika U K;Sheeba Babu;Jubilant J Kizhakkethottam
Author_Institution :
St. Joseph´s College of Engineering &
fYear :
2015
Firstpage :
1
Lastpage :
3
Abstract :
Feature selection has two important roles in the neuroimaging based classification. It possesses increased classification accuracy by eliminating the irrelevant features and identifying the best features for the discrimination of classes. Many approaches implemented for the feature selection in the context of neuroimaging. The development of feature selection methods is an active area of research. Now days a variety of feature selection methods are available so it is difficult to compare their characteristics To know this, different feature selection methods are compared and make a detailed study on this. This paper contains a comparison study among different feature selection methods and identification of merits and demerits.
Keywords :
"Accuracy","Neuroimaging","Context","Support vector machines","Redundancy","Prognostics and health management","Bioinformatics"
Publisher :
ieee
Conference_Titel :
Soft-Computing and Networks Security (ICSNS), 2015 International Conference on
Print_ISBN :
978-1-4799-1752-5
Type :
conf
DOI :
10.1109/ICSNS.2015.7292401
Filename :
7292401
Link To Document :
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