Domain Adaptation with Data Uncertainty Measure Based on Evidence Theory
Domain adaptation aims to learn a classifier for a target domain task by using related labeled data from the source domain. Because source domain data and target domain task may be mismatched, there is an uncertainty of source domain data with respect to the target domain task. Ignoring the uncertai...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/24/7/966 |
| _version_ | 1797433602407923712 |
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| author | Ying Lv Bofeng Zhang Guobing Zou Xiaodong Yue Zhikang Xu Haiyan Li |
| author_facet | Ying Lv Bofeng Zhang Guobing Zou Xiaodong Yue Zhikang Xu Haiyan Li |
| author_sort | Ying Lv |
| collection | DOAJ |
| description | Domain adaptation aims to learn a classifier for a target domain task by using related labeled data from the source domain. Because source domain data and target domain task may be mismatched, there is an uncertainty of source domain data with respect to the target domain task. Ignoring the uncertainty may lead to models with unreliable and suboptimal classification results for the target domain task. However, most previous works focus on reducing the gap in data distribution between the source and target domains. They do not consider the uncertainty of source domain data about the target domain task and cannot apply the uncertainty to learn an adaptive classifier. Aimed at this problem, we revisit the domain adaptation from source domain data uncertainty based on evidence theory and thereby devise an adaptive classifier with the uncertainty measure. Based on evidence theory, we first design an evidence net to estimate the uncertainty of source domain data about the target domain task. Second, we design a general loss function with the uncertainty measure for the adaptive classifier and extend the loss function to support vector machine. Finally, numerical experiments on simulation datasets and real-world applications are given to comprehensively demonstrate the effectiveness of the adaptive classifier with the uncertainty measure. |
| first_indexed | 2024-03-09T10:19:20Z |
| format | Article |
| id | doaj.art-86470f2f5ca44d429c943616e399254a |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T10:19:20Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-86470f2f5ca44d429c943616e399254a2023-12-01T22:06:55ZengMDPI AGEntropy1099-43002022-07-0124796610.3390/e24070966Domain Adaptation with Data Uncertainty Measure Based on Evidence TheoryYing Lv0Bofeng Zhang1Guobing Zou2Xiaodong Yue3Zhikang Xu4Haiyan Li5School of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaSchool of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai 201209, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaSchool of Computer Engineering and Science, Shanghai University, Shanghai 200444, ChinaSchool of Computer Science and Technology, Kashi University, Kashi 844006, ChinaDomain adaptation aims to learn a classifier for a target domain task by using related labeled data from the source domain. Because source domain data and target domain task may be mismatched, there is an uncertainty of source domain data with respect to the target domain task. Ignoring the uncertainty may lead to models with unreliable and suboptimal classification results for the target domain task. However, most previous works focus on reducing the gap in data distribution between the source and target domains. They do not consider the uncertainty of source domain data about the target domain task and cannot apply the uncertainty to learn an adaptive classifier. Aimed at this problem, we revisit the domain adaptation from source domain data uncertainty based on evidence theory and thereby devise an adaptive classifier with the uncertainty measure. Based on evidence theory, we first design an evidence net to estimate the uncertainty of source domain data about the target domain task. Second, we design a general loss function with the uncertainty measure for the adaptive classifier and extend the loss function to support vector machine. Finally, numerical experiments on simulation datasets and real-world applications are given to comprehensively demonstrate the effectiveness of the adaptive classifier with the uncertainty measure.https://www.mdpi.com/1099-4300/24/7/966domain adaptationtransfer learningevidence theoryuncertainty measure |
| spellingShingle | Ying Lv Bofeng Zhang Guobing Zou Xiaodong Yue Zhikang Xu Haiyan Li Domain Adaptation with Data Uncertainty Measure Based on Evidence Theory Entropy domain adaptation transfer learning evidence theory uncertainty measure |
| title | Domain Adaptation with Data Uncertainty Measure Based on Evidence Theory |
| title_full | Domain Adaptation with Data Uncertainty Measure Based on Evidence Theory |
| title_fullStr | Domain Adaptation with Data Uncertainty Measure Based on Evidence Theory |
| title_full_unstemmed | Domain Adaptation with Data Uncertainty Measure Based on Evidence Theory |
| title_short | Domain Adaptation with Data Uncertainty Measure Based on Evidence Theory |
| title_sort | domain adaptation with data uncertainty measure based on evidence theory |
| topic | domain adaptation transfer learning evidence theory uncertainty measure |
| url | https://www.mdpi.com/1099-4300/24/7/966 |
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