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- W2067321962 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." @default.
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- W2067321962 date "2012-07-01" @default.
- W2067321962 modified "2023-09-23" @default.
- W2067321962 title "Feature selection by high dimensional model representation and its application to remote sensing" @default.
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- W2067321962 doi "https://doi.org/10.1109/igarss.2012.6352504" @default.
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