Multi-label feature selection algorithm based on joint mutual information of max-relevance and min-redundancy
Feature selection has played an important role in machine learning and artificial intelligence in the past decades.Many existing feature selection algorithm have chosen some redundant and irrelevant features,which is leading to overestimation of some features.Moreover,more features will significantl...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2018-05-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018082/ |
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| _version_ | 1841539419459289088 |
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| author | Li ZHANG Cong WANG |
| author_facet | Li ZHANG Cong WANG |
| author_sort | Li ZHANG |
| collection | DOAJ |
| description | Feature selection has played an important role in machine learning and artificial intelligence in the past decades.Many existing feature selection algorithm have chosen some redundant and irrelevant features,which is leading to overestimation of some features.Moreover,more features will significantly slow down the speed of machine learning and lead to classification over-fitting.Therefore,a new nonlinear feature selection algorithm based on forward search was proposed.The algorithm used the theory of mutual information and mutual information to find the optimal subset associated with multi-task labels and reduced the computational complexity.Compared with the experimental results of nine datasets and four different classifiers in UCI,the proposed algorithm is superior to the feature set selected by the original feature set and other feature selection algorithms. |
| format | Article |
| id | doaj-art-4c85566d340b449388d82878b2628bd1 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2018-05-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-4c85566d340b449388d82878b2628bd12025-01-14T07:14:47ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2018-05-013911112259718280Multi-label feature selection algorithm based on joint mutual information of max-relevance and min-redundancyLi ZHANGCong WANGFeature selection has played an important role in machine learning and artificial intelligence in the past decades.Many existing feature selection algorithm have chosen some redundant and irrelevant features,which is leading to overestimation of some features.Moreover,more features will significantly slow down the speed of machine learning and lead to classification over-fitting.Therefore,a new nonlinear feature selection algorithm based on forward search was proposed.The algorithm used the theory of mutual information and mutual information to find the optimal subset associated with multi-task labels and reduced the computational complexity.Compared with the experimental results of nine datasets and four different classifiers in UCI,the proposed algorithm is superior to the feature set selected by the original feature set and other feature selection algorithms.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018082/feature selectionconditional mutual informationfeature interactionfeature relevancefeature redundancy |
| spellingShingle | Li ZHANG Cong WANG Multi-label feature selection algorithm based on joint mutual information of max-relevance and min-redundancy Tongxin xuebao feature selection conditional mutual information feature interaction feature relevance feature redundancy |
| title | Multi-label feature selection algorithm based on joint mutual information of max-relevance and min-redundancy |
| title_full | Multi-label feature selection algorithm based on joint mutual information of max-relevance and min-redundancy |
| title_fullStr | Multi-label feature selection algorithm based on joint mutual information of max-relevance and min-redundancy |
| title_full_unstemmed | Multi-label feature selection algorithm based on joint mutual information of max-relevance and min-redundancy |
| title_short | Multi-label feature selection algorithm based on joint mutual information of max-relevance and min-redundancy |
| title_sort | multi label feature selection algorithm based on joint mutual information of max relevance and min redundancy |
| topic | feature selection conditional mutual information feature interaction feature relevance feature redundancy |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2018082/ |
| work_keys_str_mv | AT lizhang multilabelfeatureselectionalgorithmbasedonjointmutualinformationofmaxrelevanceandminredundancy AT congwang multilabelfeatureselectionalgorithmbasedonjointmutualinformationofmaxrelevanceandminredundancy |