A Classification Model Based on Interval Rule Inference Network with Interpretability
Interpretability requirements, complex uncertain data processing, and limited training data are characteristics of classification in some real industry applications. The interval belief rule base (IBRB) can deal with various types of uncertainty and provides high interpretability. However, there is...
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/649 |
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| author | Yunxia Zhang Yiming Zhong Xiaochang Wu Jing Bai |
| author_facet | Yunxia Zhang Yiming Zhong Xiaochang Wu Jing Bai |
| author_sort | Yunxia Zhang |
| collection | DOAJ |
| description | Interpretability requirements, complex uncertain data processing, and limited training data are characteristics of classification in some real industry applications. The interval belief rule base (IBRB) can deal with various types of uncertainty and provides high interpretability. However, there is a large number of parameters in IBRB, which makes it difficult for experts to accurately set them manually, limiting its application scope. To address this issue, this paper proposes an interval rule inference network (IRIN) with interpretability for classification models to automatically generate IBRB through integrating the ideas of the IBRB and the neural network. Firstly, hybrid data with different types are transformed into an interval belief distribution for automatic generation processing. Secondly, the interval evidence reasoning method is utilized as the inference engine to transfer information ensuring the process’s interpretability. Finally, a reasonable IBRB is generated automatically by updating the parameters by employing the learning engine in the neural network. Moreover, the differentiability of the interval evidence reasoning method in the IRIN is proved as a theoretical foundation of the IRIN, and an interpretability analysis of the IRIN’s structures is discussed. Experimental results demonstrate that the proposed method possesses high interpretability, enhancing the reliability of classification and maintaining the accuracy. Its application in an actual engineering case illustrates that it is particularly suitable for engineering problems where the explanation of results is a critical requirement. |
| format | Article |
| id | doaj-art-fa8384e87dd742ad8cfe3a94ec4ccbbf |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-fa8384e87dd742ad8cfe3a94ec4ccbbf2025-01-24T13:20:17ZengMDPI AGApplied Sciences2076-34172025-01-0115264910.3390/app15020649A Classification Model Based on Interval Rule Inference Network with InterpretabilityYunxia Zhang0Yiming Zhong1Xiaochang Wu2Jing Bai3School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 050043, ChinaSchool of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 050043, ChinaSchool of Computer, Guangdong University of Petrochemical Technology, Maoming 525000, ChinaSchool of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 050043, ChinaInterpretability requirements, complex uncertain data processing, and limited training data are characteristics of classification in some real industry applications. The interval belief rule base (IBRB) can deal with various types of uncertainty and provides high interpretability. However, there is a large number of parameters in IBRB, which makes it difficult for experts to accurately set them manually, limiting its application scope. To address this issue, this paper proposes an interval rule inference network (IRIN) with interpretability for classification models to automatically generate IBRB through integrating the ideas of the IBRB and the neural network. Firstly, hybrid data with different types are transformed into an interval belief distribution for automatic generation processing. Secondly, the interval evidence reasoning method is utilized as the inference engine to transfer information ensuring the process’s interpretability. Finally, a reasonable IBRB is generated automatically by updating the parameters by employing the learning engine in the neural network. Moreover, the differentiability of the interval evidence reasoning method in the IRIN is proved as a theoretical foundation of the IRIN, and an interpretability analysis of the IRIN’s structures is discussed. Experimental results demonstrate that the proposed method possesses high interpretability, enhancing the reliability of classification and maintaining the accuracy. Its application in an actual engineering case illustrates that it is particularly suitable for engineering problems where the explanation of results is a critical requirement.https://www.mdpi.com/2076-3417/15/2/649interval rule inference networkrule inference networkinterval belief rule baseinterval uncertaintyinterpretability |
| spellingShingle | Yunxia Zhang Yiming Zhong Xiaochang Wu Jing Bai A Classification Model Based on Interval Rule Inference Network with Interpretability Applied Sciences interval rule inference network rule inference network interval belief rule base interval uncertainty interpretability |
| title | A Classification Model Based on Interval Rule Inference Network with Interpretability |
| title_full | A Classification Model Based on Interval Rule Inference Network with Interpretability |
| title_fullStr | A Classification Model Based on Interval Rule Inference Network with Interpretability |
| title_full_unstemmed | A Classification Model Based on Interval Rule Inference Network with Interpretability |
| title_short | A Classification Model Based on Interval Rule Inference Network with Interpretability |
| title_sort | classification model based on interval rule inference network with interpretability |
| topic | interval rule inference network rule inference network interval belief rule base interval uncertainty interpretability |
| url | https://www.mdpi.com/2076-3417/15/2/649 |
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