Open-Set Recognition Model Based on Negative-Class Sample Feature Enhancement Learning Algorithm
In order to solve the problem that the F1-measure value and the AUROC value of some classical open-set classifier methods do not exceed 40% in high-openness scenarios, this paper proposes an algorithm combining negative-class feature enhancement learning and a Weibull distribution based on an extrem...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/24/4725 |
| _version_ | 1797456441159712768 |
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| author | Guowei Yang Shijie Zhou Minghua Wan |
| author_facet | Guowei Yang Shijie Zhou Minghua Wan |
| author_sort | Guowei Yang |
| collection | DOAJ |
| description | In order to solve the problem that the F1-measure value and the AUROC value of some classical open-set classifier methods do not exceed 40% in high-openness scenarios, this paper proposes an algorithm combining negative-class feature enhancement learning and a Weibull distribution based on an extreme value theory representation method, which can effectively reduce the risk of open space in open-set scenarios. Firstly, the solution uses the negative-class sample feature enhancement learning algorithm to generate the negative sample point set of similar features and then compute the corresponding negative-class sample feature segmentation hypersphere. Secondly, the paired Weibull distributions from positive and negative samples are established based on the corresponding negative-class sample feature segmentation hypersphere of each class. Finally, solutions for non-linear multi-class classifications are constructed by using the Weibull and reverse Weibull distributions. Experiments on classic open datasets such as the open dataset of letter recognition, the Caltech256 open dataset, and the CIFAR100 open dataset show that when the openness is greater than 60%, the performance of the proposed method is significantly higher than other open-set support vector classifier algorithms, and the average is more than 7% higher. |
| first_indexed | 2024-03-09T16:07:52Z |
| format | Article |
| id | doaj.art-24e8d0df85e641fdabf5a659a51e0ffc |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T16:07:52Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-24e8d0df85e641fdabf5a659a51e0ffc2023-11-24T16:28:38ZengMDPI AGMathematics2227-73902022-12-011024472510.3390/math10244725Open-Set Recognition Model Based on Negative-Class Sample Feature Enhancement Learning AlgorithmGuowei Yang0Shijie Zhou1Minghua Wan2School of Computer Science (School of Intelligent Auditing), Nanjing Audit University, Nanjing 211815, ChinaSchool of Computer Science (School of Intelligent Auditing), Nanjing Audit University, Nanjing 211815, ChinaSchool of Computer Science (School of Intelligent Auditing), Nanjing Audit University, Nanjing 211815, ChinaIn order to solve the problem that the F1-measure value and the AUROC value of some classical open-set classifier methods do not exceed 40% in high-openness scenarios, this paper proposes an algorithm combining negative-class feature enhancement learning and a Weibull distribution based on an extreme value theory representation method, which can effectively reduce the risk of open space in open-set scenarios. Firstly, the solution uses the negative-class sample feature enhancement learning algorithm to generate the negative sample point set of similar features and then compute the corresponding negative-class sample feature segmentation hypersphere. Secondly, the paired Weibull distributions from positive and negative samples are established based on the corresponding negative-class sample feature segmentation hypersphere of each class. Finally, solutions for non-linear multi-class classifications are constructed by using the Weibull and reverse Weibull distributions. Experiments on classic open datasets such as the open dataset of letter recognition, the Caltech256 open dataset, and the CIFAR100 open dataset show that when the openness is greater than 60%, the performance of the proposed method is significantly higher than other open-set support vector classifier algorithms, and the average is more than 7% higher.https://www.mdpi.com/2227-7390/10/24/4725open-set recognitionenhancement learningfeature enhancementextreme value distribution theoryWeibull distribution |
| spellingShingle | Guowei Yang Shijie Zhou Minghua Wan Open-Set Recognition Model Based on Negative-Class Sample Feature Enhancement Learning Algorithm Mathematics open-set recognition enhancement learning feature enhancement extreme value distribution theory Weibull distribution |
| title | Open-Set Recognition Model Based on Negative-Class Sample Feature Enhancement Learning Algorithm |
| title_full | Open-Set Recognition Model Based on Negative-Class Sample Feature Enhancement Learning Algorithm |
| title_fullStr | Open-Set Recognition Model Based on Negative-Class Sample Feature Enhancement Learning Algorithm |
| title_full_unstemmed | Open-Set Recognition Model Based on Negative-Class Sample Feature Enhancement Learning Algorithm |
| title_short | Open-Set Recognition Model Based on Negative-Class Sample Feature Enhancement Learning Algorithm |
| title_sort | open set recognition model based on negative class sample feature enhancement learning algorithm |
| topic | open-set recognition enhancement learning feature enhancement extreme value distribution theory Weibull distribution |
| url | https://www.mdpi.com/2227-7390/10/24/4725 |
| work_keys_str_mv | AT guoweiyang opensetrecognitionmodelbasedonnegativeclasssamplefeatureenhancementlearningalgorithm AT shijiezhou opensetrecognitionmodelbasedonnegativeclasssamplefeatureenhancementlearningalgorithm AT minghuawan opensetrecognitionmodelbasedonnegativeclasssamplefeatureenhancementlearningalgorithm |