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|Title:||Cost-sensitive feature selection by optimizing F-measures||Authors:||Liu, Meng
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2017||Source:||Liu, M., Xu, C., Luo, Y., Xu, C., Wen, Y., & Tao, D. (2018). Cost-sensitive feature selection by optimizing F-measures. IEEE Transactions on Image Processing, 27(3), 1323-1335. doi:10.1109/TIP.2017.2781298||Journal:||IEEE Transactions on Image Processing||Abstract:||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 the majority class. Considering that F-measure is a more reasonable performance measure than accuracy for imbalanced data, this paper presents an effective feature selection algorithm that explores the class imbalance issue by optimizing F-measures. Since F-measure optimization can be decomposed into a series of cost-sensitive classification problems, we investigate the cost-sensitive feature selection by generating and assigning different costs to each class with rigorous theory guidance. After solving a series of cost-sensitive feature selection problems, features corresponding to the best F-measure will be selected. In this way, the selected features will fully represent the properties of all classes. Experimental results on popular benchmarks and challenging real-world data sets demonstrate the significance of cost-sensitive feature selection for the imbalanced data setting and validate the effectiveness of the proposed method.||URI:||https://hdl.handle.net/10356/142330||ISSN:||1057-7149||DOI:||10.1109/TIP.2017.2781298||Rights:||© 2017 IEEE. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SCSE Journal Articles|
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