SAB: Self-Adaptive Bias
Curriculum learning is a method of prioritizing learning data to improve learning performance. In this paper, we propose a new algorithm that determines how to select learning data and when to start and stop curriculum learning by considering learning errors. We use entropy to select data samples wi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | AI |
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| Online Access: | https://www.mdpi.com/2673-2688/5/4/133 |
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| author | Suchan Choi Jinyoung Oh Jeong-Won Cha |
| author_facet | Suchan Choi Jinyoung Oh Jeong-Won Cha |
| author_sort | Suchan Choi |
| collection | DOAJ |
| description | Curriculum learning is a method of prioritizing learning data to improve learning performance. In this paper, we propose a new algorithm that determines how to select learning data and when to start and stop curriculum learning by considering learning errors. We use entropy to select data samples with less consistent predictions and automatically determine the warming-up period based on the characteristics of the data. Additionally, to mitigate learning bias, we introduced a variable that adjusts the range of sample selection according to the progress of the training. To validate our method, we conducted extensive experiments on both balanced and imbalanced data classification tasks, and our proposed approach showed an average improvement of about 1.8%, with a maximum improvement of up to 4.4%, compared to previously suggested methods. |
| format | Article |
| id | doaj-art-c82171f604d54ee7a26e2db48d152a17 |
| institution | DOAJ |
| issn | 2673-2688 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj-art-c82171f604d54ee7a26e2db48d152a172025-08-20T02:53:22ZengMDPI AGAI2673-26882024-12-01542761277210.3390/ai5040133SAB: Self-Adaptive BiasSuchan Choi0Jinyoung Oh1Jeong-Won Cha2Department of Computer Engineering, Changwon National University, Changwon 51140, Republic of KoreaDepartment of Computer Engineering, Changwon National University, Changwon 51140, Republic of KoreaDepartment of Computer Engineering, Changwon National University, Changwon 51140, Republic of KoreaCurriculum learning is a method of prioritizing learning data to improve learning performance. In this paper, we propose a new algorithm that determines how to select learning data and when to start and stop curriculum learning by considering learning errors. We use entropy to select data samples with less consistent predictions and automatically determine the warming-up period based on the characteristics of the data. Additionally, to mitigate learning bias, we introduced a variable that adjusts the range of sample selection according to the progress of the training. To validate our method, we conducted extensive experiments on both balanced and imbalanced data classification tasks, and our proposed approach showed an average improvement of about 1.8%, with a maximum improvement of up to 4.4%, compared to previously suggested methods.https://www.mdpi.com/2673-2688/5/4/133curriculum learningadaptive batch selectionpre-trained models |
| spellingShingle | Suchan Choi Jinyoung Oh Jeong-Won Cha SAB: Self-Adaptive Bias AI curriculum learning adaptive batch selection pre-trained models |
| title | SAB: Self-Adaptive Bias |
| title_full | SAB: Self-Adaptive Bias |
| title_fullStr | SAB: Self-Adaptive Bias |
| title_full_unstemmed | SAB: Self-Adaptive Bias |
| title_short | SAB: Self-Adaptive Bias |
| title_sort | sab self adaptive bias |
| topic | curriculum learning adaptive batch selection pre-trained models |
| url | https://www.mdpi.com/2673-2688/5/4/133 |
| work_keys_str_mv | AT suchanchoi sabselfadaptivebias AT jinyoungoh sabselfadaptivebias AT jeongwoncha sabselfadaptivebias |