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: | , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/142330 |
| _version_ | 1826121836462604288 |
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| author | Liu, Meng Xu, Chang Luo, Yong Xu, Chao Wen, Yonggang Tao, Dacheng |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Liu, Meng Xu, Chang Luo, Yong Xu, Chao Wen, Yonggang Tao, Dacheng |
| author_sort | Liu, Meng |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T05:38:42Z |
| format | Journal Article |
| id | ntu-10356/142330 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:38:42Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1423302020-06-19T04:35:57Z Cost-sensitive feature selection by optimizing F-measures Liu, Meng Xu, Chang Luo, Yong Xu, Chao Wen, Yonggang Tao, Dacheng School of Computer Science and Engineering Engineering::Computer science and engineering Feature Selection Cost-sensitive 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. MOE (Min. of Education, S’pore) 2020-06-19T04:35:57Z 2020-06-19T04:35:57Z 2017 Journal Article 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 1057-7149 https://hdl.handle.net/10356/142330 10.1109/TIP.2017.2781298 29990221 2-s2.0-85038397694 3 27 1323 1335 en IEEE Transactions on Image Processing © 2017 IEEE. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Feature Selection Cost-sensitive Liu, Meng Xu, Chang Luo, Yong Xu, Chao Wen, Yonggang Tao, Dacheng Cost-sensitive feature selection by optimizing F-measures |
| title | Cost-sensitive feature selection by optimizing F-measures |
| title_full | Cost-sensitive feature selection by optimizing F-measures |
| title_fullStr | Cost-sensitive feature selection by optimizing F-measures |
| title_full_unstemmed | Cost-sensitive feature selection by optimizing F-measures |
| title_short | Cost-sensitive feature selection by optimizing F-measures |
| title_sort | cost sensitive feature selection by optimizing f measures |
| topic | Engineering::Computer science and engineering Feature Selection Cost-sensitive |
| url | https://hdl.handle.net/10356/142330 |
| work_keys_str_mv | AT liumeng costsensitivefeatureselectionbyoptimizingfmeasures AT xuchang costsensitivefeatureselectionbyoptimizingfmeasures AT luoyong costsensitivefeatureselectionbyoptimizingfmeasures AT xuchao costsensitivefeatureselectionbyoptimizingfmeasures AT wenyonggang costsensitivefeatureselectionbyoptimizingfmeasures AT taodacheng costsensitivefeatureselectionbyoptimizingfmeasures |