Feature Subset Selection Based on Variable Precision Neighborhood Rough Sets
Rough sets have been widely used in the fields of machine learning and feature selection. However, the classical rough sets have the problems of difficultly dealing with real-value data and weakly fault tolerance. In this paper, by introducing a neighborhood rough set model, the values of decision s...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2021-01-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/125951141/view |
| _version_ | 1828393258471915520 |
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| author | Yingyue Chen Yumin Chen |
| author_facet | Yingyue Chen Yumin Chen |
| author_sort | Yingyue Chen |
| collection | DOAJ |
| description | Rough sets have been widely used in the fields of machine learning and feature selection. However, the classical rough sets have the problems of difficultly dealing with real-value data and weakly fault tolerance. In this paper, by introducing a neighborhood rough set model, the values of decision systems are granulated into some condition and decision neighborhood granules. A concept of neighborhood granular swarm is defined in a decision system. Then the sizes of a neighborhood granule and a neighborhood granular swarm are also given. In order to enhance the fault-tolerant ability of classification systems, we define some concepts of granule inclusion, variable precision neighborhood approximation sets and positive region. We propose a variable precision neighborhood rough set model, and analyze its property. Furthermore, based on the positive region of a variable precision neighborhood, we give the significance of an attribute and use it to select feature subsets. A feature subset selection algorithm to the variable precision neighborhood rough sets is designed. Finally, the feature selection algorithm is carried out on the UCI datasets, and the selected features are tested by the support vector machine (SVM) classification algorithm. Theoretical analysis and experiments show that the proposed method can find the effective and compact feature subsets, which have abilities of fault tolerance. |
| first_indexed | 2024-12-10T07:35:59Z |
| format | Article |
| id | doaj.art-1716375fef5d4b72ad708c2c41e7d2fe |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-12-10T07:35:59Z |
| publishDate | 2021-01-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-1716375fef5d4b72ad708c2c41e7d2fe2022-12-22T01:57:25ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832021-01-0114110.2991/ijcis.d.210106.003Feature Subset Selection Based on Variable Precision Neighborhood Rough SetsYingyue ChenYumin ChenRough sets have been widely used in the fields of machine learning and feature selection. However, the classical rough sets have the problems of difficultly dealing with real-value data and weakly fault tolerance. In this paper, by introducing a neighborhood rough set model, the values of decision systems are granulated into some condition and decision neighborhood granules. A concept of neighborhood granular swarm is defined in a decision system. Then the sizes of a neighborhood granule and a neighborhood granular swarm are also given. In order to enhance the fault-tolerant ability of classification systems, we define some concepts of granule inclusion, variable precision neighborhood approximation sets and positive region. We propose a variable precision neighborhood rough set model, and analyze its property. Furthermore, based on the positive region of a variable precision neighborhood, we give the significance of an attribute and use it to select feature subsets. A feature subset selection algorithm to the variable precision neighborhood rough sets is designed. Finally, the feature selection algorithm is carried out on the UCI datasets, and the selected features are tested by the support vector machine (SVM) classification algorithm. Theoretical analysis and experiments show that the proposed method can find the effective and compact feature subsets, which have abilities of fault tolerance.https://www.atlantis-press.com/article/125951141/viewRough setsVariable precision neighborhood rough setsAttribute reductionFeature selectionNeighborhood systems |
| spellingShingle | Yingyue Chen Yumin Chen Feature Subset Selection Based on Variable Precision Neighborhood Rough Sets International Journal of Computational Intelligence Systems Rough sets Variable precision neighborhood rough sets Attribute reduction Feature selection Neighborhood systems |
| title | Feature Subset Selection Based on Variable Precision Neighborhood Rough Sets |
| title_full | Feature Subset Selection Based on Variable Precision Neighborhood Rough Sets |
| title_fullStr | Feature Subset Selection Based on Variable Precision Neighborhood Rough Sets |
| title_full_unstemmed | Feature Subset Selection Based on Variable Precision Neighborhood Rough Sets |
| title_short | Feature Subset Selection Based on Variable Precision Neighborhood Rough Sets |
| title_sort | feature subset selection based on variable precision neighborhood rough sets |
| topic | Rough sets Variable precision neighborhood rough sets Attribute reduction Feature selection Neighborhood systems |
| url | https://www.atlantis-press.com/article/125951141/view |
| work_keys_str_mv | AT yingyuechen featuresubsetselectionbasedonvariableprecisionneighborhoodroughsets AT yuminchen featuresubsetselectionbasedonvariableprecisionneighborhoodroughsets |