Incremental Data Stream Classification with Adaptive Multi-Task Multi-View Learning
With the enhancement of data collection capabilities, massive streaming data have been accumulated in numerous application scenarios. Specifically, the issue of classifying data streams based on mobile sensors can be formalized as a multi-task multi-view learning problem with a specific task compris...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2024-03-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2023.9020006 |
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| _version_ | 1832568842785652736 |
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| author | Jun Wang Maiwang Shi Xiao Zhang Yan Li Yunsheng Yuan Chenglei Yang Dongxiao Yu |
| author_facet | Jun Wang Maiwang Shi Xiao Zhang Yan Li Yunsheng Yuan Chenglei Yang Dongxiao Yu |
| author_sort | Jun Wang |
| collection | DOAJ |
| description | With the enhancement of data collection capabilities, massive streaming data have been accumulated in numerous application scenarios. Specifically, the issue of classifying data streams based on mobile sensors can be formalized as a multi-task multi-view learning problem with a specific task comprising multiple views with shared features collected from multiple sensors. Existing incremental learning methods are often single-task single-view, which cannot learn shared representations between relevant tasks and views. An adaptive multi-task multi-view incremental learning framework for data stream classification called MTMVIS is proposed to address the above challenges, utilizing the idea of multi-task multi-view learning. Specifically, the attention mechanism is first used to align different sensor data of different views. In addition, MTMVIS uses adaptive Fisher regularization from the perspective of multi-task multi-view learning to overcome catastrophic forgetting in incremental learning. Results reveal that the proposed framework outperforms state-of-the-art methods based on the experiments on two different datasets with other baselines. |
| format | Article |
| id | doaj-art-5d5c3b20a35f4a30bed8ebb0e25d998c |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-5d5c3b20a35f4a30bed8ebb0e25d998c2025-02-03T00:17:02ZengTsinghua University PressBig Data Mining and Analytics2096-06542024-03-01718710610.26599/BDMA.2023.9020006Incremental Data Stream Classification with Adaptive Multi-Task Multi-View LearningJun Wang0Maiwang Shi1Xiao Zhang2Yan Li3Yunsheng Yuan4Chenglei Yang5Dongxiao Yu6School of Computer Science and Technology, Shandong University, Qingdao 266237, ChinaSchool of Computer Science and Technology, Shandong University, Qingdao 266237, ChinaSchool of Computer Science and Technology, Shandong University, Qingdao 266237, ChinaSchool of Computer Science and Technology, Shandong University, Qingdao 266237, ChinaSchool of Computer Science and Technology, Shandong University, Qingdao 266237, ChinaSchool of Software, Shandong University, Jinan 250101, ChinaSchool of Computer Science and Technology, Shandong University, Qingdao 266237, ChinaWith the enhancement of data collection capabilities, massive streaming data have been accumulated in numerous application scenarios. Specifically, the issue of classifying data streams based on mobile sensors can be formalized as a multi-task multi-view learning problem with a specific task comprising multiple views with shared features collected from multiple sensors. Existing incremental learning methods are often single-task single-view, which cannot learn shared representations between relevant tasks and views. An adaptive multi-task multi-view incremental learning framework for data stream classification called MTMVIS is proposed to address the above challenges, utilizing the idea of multi-task multi-view learning. Specifically, the attention mechanism is first used to align different sensor data of different views. In addition, MTMVIS uses adaptive Fisher regularization from the perspective of multi-task multi-view learning to overcome catastrophic forgetting in incremental learning. Results reveal that the proposed framework outperforms state-of-the-art methods based on the experiments on two different datasets with other baselines.https://www.sciopen.com/article/10.26599/BDMA.2023.9020006data stream classificationmobile sensorsmulti-task multi-view learningincremental learning |
| spellingShingle | Jun Wang Maiwang Shi Xiao Zhang Yan Li Yunsheng Yuan Chenglei Yang Dongxiao Yu Incremental Data Stream Classification with Adaptive Multi-Task Multi-View Learning Big Data Mining and Analytics data stream classification mobile sensors multi-task multi-view learning incremental learning |
| title | Incremental Data Stream Classification with Adaptive Multi-Task Multi-View Learning |
| title_full | Incremental Data Stream Classification with Adaptive Multi-Task Multi-View Learning |
| title_fullStr | Incremental Data Stream Classification with Adaptive Multi-Task Multi-View Learning |
| title_full_unstemmed | Incremental Data Stream Classification with Adaptive Multi-Task Multi-View Learning |
| title_short | Incremental Data Stream Classification with Adaptive Multi-Task Multi-View Learning |
| title_sort | incremental data stream classification with adaptive multi task multi view learning |
| topic | data stream classification mobile sensors multi-task multi-view learning incremental learning |
| url | https://www.sciopen.com/article/10.26599/BDMA.2023.9020006 |
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