Neighborhood conditional mutual information entropy attribute reduction algorithm for hybrid data
Attribute reduction is an important research content of the rough set theory.Its main purpose is to eliminate irrelevant attributes in information systems, reduce data dimensions and improve data knowledge discovery performance.However, most of the attribute reduction methods based on a rough set do...
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China InfoCom Media Group
2022-07-01
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| Series: | 大数据 |
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| Online Access: | http://www.j-bigdataresearch.com.cn/thesisDetails#10.11959/j.issn.2096-0271.2022066 |
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| author | Haibo LAN |
| author_facet | Haibo LAN |
| author_sort | Haibo LAN |
| collection | DOAJ |
| description | Attribute reduction is an important research content of the rough set theory.Its main purpose is to eliminate irrelevant attributes in information systems, reduce data dimensions and improve data knowledge discovery performance.However, most of the attribute reduction methods based on a rough set do not consider the dependence between attributes, which makes the final attribute reduction result have some redundant attributes.An attribute reduction algorithm based on neighborhood conditional mutual information entropy was proposed.Firstly, based on the traditional neighborhood entropy, a hybrid neighborhood mutual information entropy model and a hybrid neighborhood conditional mutual information entropy model were proposed for hybrid data.Then, the two entropy models were used to evaluate the attribute dependence and attribute heuristic search of the hybrid information system, and an attribute reduction algorithm was designed.Finally, through the experimental analysis of UCI data sets, it was proved that the algorithm had higher attribute reduction performance. |
| format | Article |
| id | doaj-art-6b0959c87d1a48fa89d8f3f9dae1932e |
| institution | OA Journals |
| issn | 2096-0271 |
| language | zho |
| publishDate | 2022-07-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 大数据 |
| spelling | doaj-art-6b0959c87d1a48fa89d8f3f9dae1932e2025-08-20T02:09:21ZzhoChina InfoCom Media Group大数据2096-02712022-07-01813314459541220Neighborhood conditional mutual information entropy attribute reduction algorithm for hybrid dataHaibo LANAttribute reduction is an important research content of the rough set theory.Its main purpose is to eliminate irrelevant attributes in information systems, reduce data dimensions and improve data knowledge discovery performance.However, most of the attribute reduction methods based on a rough set do not consider the dependence between attributes, which makes the final attribute reduction result have some redundant attributes.An attribute reduction algorithm based on neighborhood conditional mutual information entropy was proposed.Firstly, based on the traditional neighborhood entropy, a hybrid neighborhood mutual information entropy model and a hybrid neighborhood conditional mutual information entropy model were proposed for hybrid data.Then, the two entropy models were used to evaluate the attribute dependence and attribute heuristic search of the hybrid information system, and an attribute reduction algorithm was designed.Finally, through the experimental analysis of UCI data sets, it was proved that the algorithm had higher attribute reduction performance.http://www.j-bigdataresearch.com.cn/thesisDetails#10.11959/j.issn.2096-0271.2022066rough set;attribute reduction;neighborhood;mutual information entropy;conditional mutual information entropy |
| spellingShingle | Haibo LAN Neighborhood conditional mutual information entropy attribute reduction algorithm for hybrid data 大数据 rough set;attribute reduction;neighborhood;mutual information entropy;conditional mutual information entropy |
| title | Neighborhood conditional mutual information entropy attribute reduction algorithm for hybrid data |
| title_full | Neighborhood conditional mutual information entropy attribute reduction algorithm for hybrid data |
| title_fullStr | Neighborhood conditional mutual information entropy attribute reduction algorithm for hybrid data |
| title_full_unstemmed | Neighborhood conditional mutual information entropy attribute reduction algorithm for hybrid data |
| title_short | Neighborhood conditional mutual information entropy attribute reduction algorithm for hybrid data |
| title_sort | neighborhood conditional mutual information entropy attribute reduction algorithm for hybrid data |
| topic | rough set;attribute reduction;neighborhood;mutual information entropy;conditional mutual information entropy |
| url | http://www.j-bigdataresearch.com.cn/thesisDetails#10.11959/j.issn.2096-0271.2022066 |
| work_keys_str_mv | AT haibolan neighborhoodconditionalmutualinformationentropyattributereductionalgorithmforhybriddata |