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