Continual learning classification method with human-in-the-loop
The classification problem is essential to machine learning, often used in fault detection, condition monitoring, and behavior recognition. In recent years, due to the rapid development of incremental learning, reinforcement learning, transfer learning, and continual learning algorithms, the contrad...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | MethodsX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016123003709 |
| _version_ | 1827596409591824384 |
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| author | Jia Liu Dong Li Wangweiyi Shan Shulin Liu |
| author_facet | Jia Liu Dong Li Wangweiyi Shan Shulin Liu |
| author_sort | Jia Liu |
| collection | DOAJ |
| description | The classification problem is essential to machine learning, often used in fault detection, condition monitoring, and behavior recognition. In recent years, due to the rapid development of incremental learning, reinforcement learning, transfer learning, and continual learning algorithms, the contradiction between the classification model and new data has been alleviated. However, due to the lack of feedback, most classification algorithms take long to search and may deviate from the correct results. Because of this, we propose a continual learning classification method with human-in-the-loop (HCLCM) based on the artificial immune system. HCLCM draws lessons from the mechanism that humans can enhance immune response through various intervention technologies and brings humans into the test learning process in a supervisory role. The human experience is integrated into the test phase, and the parameters corresponding to the error identification data are adjusted online. It enables it to converge to an accurate prediction model at the lowest cost and to learn new data categories without retraining the classifier. • All necessary steps and formulas of HCLCM are provided. • HCLCM adds manual intervention to improve the classification ability of the model. • HCLCM can recognize new types of data. |
| first_indexed | 2024-03-09T03:10:09Z |
| format | Article |
| id | doaj.art-a4a7dc05d6e84ca5bca610f216e171b6 |
| institution | Directory Open Access Journal |
| issn | 2215-0161 |
| language | English |
| last_indexed | 2024-03-09T03:10:09Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj.art-a4a7dc05d6e84ca5bca610f216e171b62023-12-04T05:22:30ZengElsevierMethodsX2215-01612023-12-0111102374Continual learning classification method with human-in-the-loopJia Liu0Dong Li1Wangweiyi Shan2Shulin Liu3School of Petroleum and Natural Gas Engineering, Changzhou 213164, People's Republic of ChinaSchool of Petroleum and Natural Gas Engineering, Changzhou 213164, People's Republic of China; Corresponding author.School of Petroleum and Natural Gas Engineering, Changzhou 213164, People's Republic of ChinaSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, People's Republic of ChinaThe classification problem is essential to machine learning, often used in fault detection, condition monitoring, and behavior recognition. In recent years, due to the rapid development of incremental learning, reinforcement learning, transfer learning, and continual learning algorithms, the contradiction between the classification model and new data has been alleviated. However, due to the lack of feedback, most classification algorithms take long to search and may deviate from the correct results. Because of this, we propose a continual learning classification method with human-in-the-loop (HCLCM) based on the artificial immune system. HCLCM draws lessons from the mechanism that humans can enhance immune response through various intervention technologies and brings humans into the test learning process in a supervisory role. The human experience is integrated into the test phase, and the parameters corresponding to the error identification data are adjusted online. It enables it to converge to an accurate prediction model at the lowest cost and to learn new data categories without retraining the classifier. • All necessary steps and formulas of HCLCM are provided. • HCLCM adds manual intervention to improve the classification ability of the model. • HCLCM can recognize new types of data.http://www.sciencedirect.com/science/article/pii/S2215016123003709H-CLCM: Continual learning classification method with human-in-the-loop |
| spellingShingle | Jia Liu Dong Li Wangweiyi Shan Shulin Liu Continual learning classification method with human-in-the-loop MethodsX H-CLCM: Continual learning classification method with human-in-the-loop |
| title | Continual learning classification method with human-in-the-loop |
| title_full | Continual learning classification method with human-in-the-loop |
| title_fullStr | Continual learning classification method with human-in-the-loop |
| title_full_unstemmed | Continual learning classification method with human-in-the-loop |
| title_short | Continual learning classification method with human-in-the-loop |
| title_sort | continual learning classification method with human in the loop |
| topic | H-CLCM: Continual learning classification method with human-in-the-loop |
| url | http://www.sciencedirect.com/science/article/pii/S2215016123003709 |
| work_keys_str_mv | AT jialiu continuallearningclassificationmethodwithhumanintheloop AT dongli continuallearningclassificationmethodwithhumanintheloop AT wangweiyishan continuallearningclassificationmethodwithhumanintheloop AT shulinliu continuallearningclassificationmethodwithhumanintheloop |