Cost-sensitive feature selection by optimizing F-measures

Cost-sensitive feature selection by optimizing F-measures

Feature selection is beneficial for improving the performance of general machine learning tasks by extracting an informative subset from the high-dimensional features. Conventional feature selection methods usually ignore the class imbalance problem, thus the selected features will be biased towards...

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Bibliographic Details
Main Authors: Liu, Meng, Xu, Chang, Luo, Yong, Xu, Chao, Wen, Yonggang, Tao, Dacheng
Other Authors: School of Computer Science and Engineering
Format: Journal Article
Language:English
Published: 2020
Subjects:
Engineering::Computer science and engineering
Feature Selection
Cost-sensitive
Online Access:https://hdl.handle.net/10356/142330

Internet

https://hdl.handle.net/10356/142330

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