Hellinger distance-based stable sparse feature selection for high-dimensional class-imbalanced data

Abstract Background Feature selection in class-imbalance learning has gained increasing attention in recent years due to the massive growth of high-dimensional class-imbalanced data across many scientific fields. In addition to reducing model complexity and discovering key biomarkers, feature select...

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Bibliographic Details

Published in:	BMC Bioinformatics
Main Authors:	Guang-Hui Fu, Yuan-Jiao Wu, Min-Jie Zong, Jianxin Pan
Format:	Article
Language:	English
Published:	BMC 2020-03-01
Subjects:	Hellinger distance Class-imbalance learning Feature selection Sparse regularization
Online Access:	http://link.springer.com/article/10.1186/s12859-020-3411-3