Feature selection based on the center of gravity of BSWFMs using NEWFM

Abstract

Feature selection has commonly been used to remove irrelevant features and improve classification performance. Some of features are irrelevant to the learning process; therefore to remove these irrelevant features not only decreases training and testing times, but can also improve learning accuracy. This study proposes a novel supervised feature selection… (More)
DOI: 10.1016/j.engappai.2015.08.003

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Cite this paper

@article{Lee2015FeatureSB, title={Feature selection based on the center of gravity of BSWFMs using NEWFM}, author={Sang-Hong Lee}, journal={Eng. Appl. of AI}, year={2015}, volume={45}, pages={482-487} }