Feature selection based on weighted conditional mutual information
Feature selection is an essential step in data mining. The core of it is to analyze and quantize the relevancy and redundancy between the features and the classes. In CFR feature selection method, they rarely consider which feature to choose if two or more features have the same value using evaluati...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Emerald Publishing
2024-01-01
|
| Series: | Applied Computing and Informatics |
| Subjects: | |
| Online Access: | https://www.emerald.com/insight/content/doi/10.1016/j.aci.2019.12.003/full/pdf |
| _version_ | 1797367176550678528 |
|---|---|
| author | Hongfang Zhou Xiqian Wang Yao Zhang |
| author_facet | Hongfang Zhou Xiqian Wang Yao Zhang |
| author_sort | Hongfang Zhou |
| collection | DOAJ |
| description | Feature selection is an essential step in data mining. The core of it is to analyze and quantize the relevancy and redundancy between the features and the classes. In CFR feature selection method, they rarely consider which feature to choose if two or more features have the same value using evaluation criterion. In order to address this problem, the standard deviation is employed to adjust the importance between relevancy and redundancy. Based on this idea, a novel feature selection method named as Feature Selection Based on Weighted Conditional Mutual Information (WCFR) is introduced. Experimental results on ten datasets show that our proposed method has higher classification accuracy. |
| first_indexed | 2024-03-08T17:14:33Z |
| format | Article |
| id | doaj.art-1b42ddce0e52413abde9ba83e8594c43 |
| institution | Directory Open Access Journal |
| issn | 2634-1964 2210-8327 |
| language | English |
| last_indexed | 2024-03-08T17:14:33Z |
| publishDate | 2024-01-01 |
| publisher | Emerald Publishing |
| record_format | Article |
| series | Applied Computing and Informatics |
| spelling | doaj.art-1b42ddce0e52413abde9ba83e8594c432024-01-03T15:21:48ZengEmerald PublishingApplied Computing and Informatics2634-19642210-83272024-01-01201/2556810.1016/j.aci.2019.12.003Feature selection based on weighted conditional mutual informationHongfang Zhou0Xiqian Wang1Yao Zhang2School of Computer Science and Engineering, Xi'an University of Technology, Xi’an, ChinaSchool of Computer Science and Engineering, Xi'an University of Technology, Xi’an, ChinaSchool of Computer Science and Engineering, Xi'an University of Technology, Xi’an, ChinaFeature selection is an essential step in data mining. The core of it is to analyze and quantize the relevancy and redundancy between the features and the classes. In CFR feature selection method, they rarely consider which feature to choose if two or more features have the same value using evaluation criterion. In order to address this problem, the standard deviation is employed to adjust the importance between relevancy and redundancy. Based on this idea, a novel feature selection method named as Feature Selection Based on Weighted Conditional Mutual Information (WCFR) is introduced. Experimental results on ten datasets show that our proposed method has higher classification accuracy.https://www.emerald.com/insight/content/doi/10.1016/j.aci.2019.12.003/full/pdfFeature selectionConditional mutual informationStandard deviation |
| spellingShingle | Hongfang Zhou Xiqian Wang Yao Zhang Feature selection based on weighted conditional mutual information Applied Computing and Informatics Feature selection Conditional mutual information Standard deviation |
| title | Feature selection based on weighted conditional mutual information |
| title_full | Feature selection based on weighted conditional mutual information |
| title_fullStr | Feature selection based on weighted conditional mutual information |
| title_full_unstemmed | Feature selection based on weighted conditional mutual information |
| title_short | Feature selection based on weighted conditional mutual information |
| title_sort | feature selection based on weighted conditional mutual information |
| topic | Feature selection Conditional mutual information Standard deviation |
| url | https://www.emerald.com/insight/content/doi/10.1016/j.aci.2019.12.003/full/pdf |
| work_keys_str_mv | AT hongfangzhou featureselectionbasedonweightedconditionalmutualinformation AT xiqianwang featureselectionbasedonweightedconditionalmutualinformation AT yaozhang featureselectionbasedonweightedconditionalmutualinformation |