Cost-sensitive feature selection by optimizing F-measures

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

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...