Feature selection algorithm based on XGBoost
Feature selection in classification has always been an important but difficult problem.This kind of problem requires that feature selection algorithms can not only help classifiers to improve the classification accuracy,but also reduce the redundant features as much as possible.Therefore,in order to...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2019-10-01
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| Series: | Tongxin xuebao |
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| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019154/ |
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| _version_ | 1841539346693357568 |
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| author | Zhanshan LI Zhaogeng LIU |
| author_facet | Zhanshan LI Zhaogeng LIU |
| author_sort | Zhanshan LI |
| collection | DOAJ |
| description | Feature selection in classification has always been an important but difficult problem.This kind of problem requires that feature selection algorithms can not only help classifiers to improve the classification accuracy,but also reduce the redundant features as much as possible.Therefore,in order to solve feature selection in the classification problems better,a new wrapped feature selection algorithm XGBSFS was proposed.The thought process of building trees in XGBoost was used for reference,and the importance of features from three importance metrics was measured to avoid the limitation of single importance metric.Then the improved sequential floating forward selection (ISFFS) was applied to search the feature subset so that it had high quality.Compared with the experimental results of eight datasets in UCI,the proposed algorithm has good performance. |
| format | Article |
| id | doaj-art-521ef38568bc49b8a855efa671855d00 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2019-10-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-521ef38568bc49b8a855efa671855d002025-01-14T07:17:54ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2019-10-014010110859730232Feature selection algorithm based on XGBoostZhanshan LIZhaogeng LIUFeature selection in classification has always been an important but difficult problem.This kind of problem requires that feature selection algorithms can not only help classifiers to improve the classification accuracy,but also reduce the redundant features as much as possible.Therefore,in order to solve feature selection in the classification problems better,a new wrapped feature selection algorithm XGBSFS was proposed.The thought process of building trees in XGBoost was used for reference,and the importance of features from three importance metrics was measured to avoid the limitation of single importance metric.Then the improved sequential floating forward selection (ISFFS) was applied to search the feature subset so that it had high quality.Compared with the experimental results of eight datasets in UCI,the proposed algorithm has good performance.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019154/feature selectionXGBoostsequential floating forward selection |
| spellingShingle | Zhanshan LI Zhaogeng LIU Feature selection algorithm based on XGBoost Tongxin xuebao feature selection XGBoost sequential floating forward selection |
| title | Feature selection algorithm based on XGBoost |
| title_full | Feature selection algorithm based on XGBoost |
| title_fullStr | Feature selection algorithm based on XGBoost |
| title_full_unstemmed | Feature selection algorithm based on XGBoost |
| title_short | Feature selection algorithm based on XGBoost |
| title_sort | feature selection algorithm based on xgboost |
| topic | feature selection XGBoost sequential floating forward selection |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019154/ |
| work_keys_str_mv | AT zhanshanli featureselectionalgorithmbasedonxgboost AT zhaogengliu featureselectionalgorithmbasedonxgboost |