DocumentCode :
3649215
Title :
Feature selection by high dimensional model representation and its application to remote sensing
Author :
Gülşen Taşkın Kaya;Hüseyin Kaya;Okan K. Ersoy
Author_Institution :
Istanbul Technical Univ., Informatics Institute, Turkey
fYear :
2012
fDate :
7/1/2012 12:00:00 AM
Firstpage :
4938
Lastpage :
4941
Abstract :
As the number of feature increases, classification accuracy may decrease. Additionally, computational overload increases with a large number of features. For effective classification performance and shortened the training time, the redundant features should be eliminated before the classification process. In this paper, a new HDMR-based feature selection approach is presented, sorting the features with respect to their sensitivity coefficient calculated by HDMR sensitivity analysis. With the experiments conducted, the HDMR-based feature selection approach is competitive with sequential forward feature selection method and faster in terms of computational time, especially when dealing with datasets having a large number of features.
Keywords :
"Mathematical model","Feature extraction","Sensitivity","Computational modeling","Training","Hyperspectral sensors"
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International
ISSN :
2153-6996
Print_ISBN :
978-1-4673-1160-1
Electronic_ISBN :
2153-7003
Type :
conf
DOI :
10.1109/IGARSS.2012.6352504
Filename :
6352504
Link To Document :
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