Group Feature Screening Based on Information Gain Ratio for Ultrahigh-Dimensional Data
Most model-free feature screening approaches focus on the -individual predictor; therefore, they are not able to incorporate structured predictors like grouped variables. In this article, we propose a group screening procedure via the information gain ratio for a classification model, which is a dir...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2022/1600986 |
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| _version_ | 1832567742543167488 |
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| author | Zhongzheng Wang Guangming Deng Jianqi Yu |
| author_facet | Zhongzheng Wang Guangming Deng Jianqi Yu |
| author_sort | Zhongzheng Wang |
| collection | DOAJ |
| description | Most model-free feature screening approaches focus on the -individual predictor; therefore, they are not able to incorporate structured predictors like grouped variables. In this article, we propose a group screening procedure via the information gain ratio for a classification model, which is a direct extension of the original sure independence screening procedure and also model-free. The proposed method yields a better screening performance and classification accuracy. It is demonstrated that the proposed group screening method possesses the sure screening property and ranking consistency properties under certain regularity conditions. Through simulation studies and real-world data analysis, we demonstrate the proposed method with the finite sample performance. |
| format | Article |
| id | doaj-art-95cca40a4a24404fa7e5424050be1848 |
| institution | Kabale University |
| issn | 2314-4785 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-95cca40a4a24404fa7e5424050be18482025-02-03T01:00:44ZengWileyJournal of Mathematics2314-47852022-01-01202210.1155/2022/1600986Group Feature Screening Based on Information Gain Ratio for Ultrahigh-Dimensional DataZhongzheng Wang0Guangming Deng1Jianqi Yu2College of ScienceCollege of ScienceCollege of ScienceMost model-free feature screening approaches focus on the -individual predictor; therefore, they are not able to incorporate structured predictors like grouped variables. In this article, we propose a group screening procedure via the information gain ratio for a classification model, which is a direct extension of the original sure independence screening procedure and also model-free. The proposed method yields a better screening performance and classification accuracy. It is demonstrated that the proposed group screening method possesses the sure screening property and ranking consistency properties under certain regularity conditions. Through simulation studies and real-world data analysis, we demonstrate the proposed method with the finite sample performance.http://dx.doi.org/10.1155/2022/1600986 |
| spellingShingle | Zhongzheng Wang Guangming Deng Jianqi Yu Group Feature Screening Based on Information Gain Ratio for Ultrahigh-Dimensional Data Journal of Mathematics |
| title | Group Feature Screening Based on Information Gain Ratio for Ultrahigh-Dimensional Data |
| title_full | Group Feature Screening Based on Information Gain Ratio for Ultrahigh-Dimensional Data |
| title_fullStr | Group Feature Screening Based on Information Gain Ratio for Ultrahigh-Dimensional Data |
| title_full_unstemmed | Group Feature Screening Based on Information Gain Ratio for Ultrahigh-Dimensional Data |
| title_short | Group Feature Screening Based on Information Gain Ratio for Ultrahigh-Dimensional Data |
| title_sort | group feature screening based on information gain ratio for ultrahigh dimensional data |
| url | http://dx.doi.org/10.1155/2022/1600986 |
| work_keys_str_mv | AT zhongzhengwang groupfeaturescreeningbasedoninformationgainratioforultrahighdimensionaldata AT guangmingdeng groupfeaturescreeningbasedoninformationgainratioforultrahighdimensionaldata AT jianqiyu groupfeaturescreeningbasedoninformationgainratioforultrahighdimensionaldata |