Extended Belief Rule-Base Optimization Base on Clustering Tree and Parameter Optimization
Extended belief rule-based (EBRB) system has a better ability to model complex problems than belief rule-based (BRB) system. However, the storage of rules in EBRB system is out of order, which leads to the low efficiency of rule retrieval during the reasoning process. Therefore, to improve the effic...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9320478/ |
| _version_ | 1818480190565646336 |
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| author | Jinhui Zhuang Jifeng Ye Nannan Chen Weijie Fang Xuecheng Fan Yanggeng Fu |
| author_facet | Jinhui Zhuang Jifeng Ye Nannan Chen Weijie Fang Xuecheng Fan Yanggeng Fu |
| author_sort | Jinhui Zhuang |
| collection | DOAJ |
| description | Extended belief rule-based (EBRB) system has a better ability to model complex problems than belief rule-based (BRB) system. However, the storage of rules in EBRB system is out of order, which leads to the low efficiency of rule retrieval during the reasoning process. Therefore, to improve the efficiency of rule retrieval, this study introduces K-means clustering tree algorithm into the construction of rule base, then proposes a multi-layer weighted reasoning approach based on K-means clustering tree. The proposed approach seeks out a path on the tree during the rule retrieval process, and then figures out several reasoning results according to the nodes on the path. These results are weighted and aggregated to obtain the final conclusion of the system, thus ensure both the efficiency of reasoning and the sufficient utilization of information. In addition, the differential evolution (DE) algorithm is used to train the parameters of EBRB system in this study. Several experiments are conducted on commonly used classification datasets from UCI, and the results are compared with some existing works of EBRB system and conventional machine learning methods. The comparison results illustrate that the proposed method can make an obvious improvement in the performance of EBRB system. |
| first_indexed | 2024-12-10T11:20:08Z |
| format | Article |
| id | doaj.art-a72d577df8f940ddbc11034b0b8fe25c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-10T11:20:08Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a72d577df8f940ddbc11034b0b8fe25c2022-12-22T01:51:00ZengIEEEIEEE Access2169-35362021-01-019125331254410.1109/ACCESS.2021.30510019320478Extended Belief Rule-Base Optimization Base on Clustering Tree and Parameter OptimizationJinhui Zhuang0https://orcid.org/0000-0001-9377-6980Jifeng Ye1https://orcid.org/0000-0002-2515-3404Nannan Chen2https://orcid.org/0000-0002-9689-2562Weijie Fang3https://orcid.org/0000-0003-2448-4786Xuecheng Fan4https://orcid.org/0000-0001-9478-3795Yanggeng Fu5https://orcid.org/0000-0002-8507-9189College of Mathematics and Computer Science, Fuzhou University, Fuzhou, ChinaCollege of Mathematics and Computer Science, Fuzhou University, Fuzhou, ChinaCollege of Mathematics and Computer Science, Fuzhou University, Fuzhou, ChinaInstitute of Decision Sciences, Fuzhou University, Fuzhou, ChinaSchool of Economics, Sichuan University, Chengdu, ChinaCollege of Mathematics and Computer Science, Fuzhou University, Fuzhou, ChinaExtended belief rule-based (EBRB) system has a better ability to model complex problems than belief rule-based (BRB) system. However, the storage of rules in EBRB system is out of order, which leads to the low efficiency of rule retrieval during the reasoning process. Therefore, to improve the efficiency of rule retrieval, this study introduces K-means clustering tree algorithm into the construction of rule base, then proposes a multi-layer weighted reasoning approach based on K-means clustering tree. The proposed approach seeks out a path on the tree during the rule retrieval process, and then figures out several reasoning results according to the nodes on the path. These results are weighted and aggregated to obtain the final conclusion of the system, thus ensure both the efficiency of reasoning and the sufficient utilization of information. In addition, the differential evolution (DE) algorithm is used to train the parameters of EBRB system in this study. Several experiments are conducted on commonly used classification datasets from UCI, and the results are compared with some existing works of EBRB system and conventional machine learning methods. The comparison results illustrate that the proposed method can make an obvious improvement in the performance of EBRB system.https://ieeexplore.ieee.org/document/9320478/Extended belief rule-based systemK-means clustering treedifferential evolutionary |
| spellingShingle | Jinhui Zhuang Jifeng Ye Nannan Chen Weijie Fang Xuecheng Fan Yanggeng Fu Extended Belief Rule-Base Optimization Base on Clustering Tree and Parameter Optimization IEEE Access Extended belief rule-based system K-means clustering tree differential evolutionary |
| title | Extended Belief Rule-Base Optimization Base on Clustering Tree and Parameter Optimization |
| title_full | Extended Belief Rule-Base Optimization Base on Clustering Tree and Parameter Optimization |
| title_fullStr | Extended Belief Rule-Base Optimization Base on Clustering Tree and Parameter Optimization |
| title_full_unstemmed | Extended Belief Rule-Base Optimization Base on Clustering Tree and Parameter Optimization |
| title_short | Extended Belief Rule-Base Optimization Base on Clustering Tree and Parameter Optimization |
| title_sort | extended belief rule base optimization base on clustering tree and parameter optimization |
| topic | Extended belief rule-based system K-means clustering tree differential evolutionary |
| url | https://ieeexplore.ieee.org/document/9320478/ |
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