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...

Full description

Saved in:
Bibliographic Details
Main Authors: Jun Wang, Maiwang Shi, Xiao Zhang, Yan Li, Yunsheng Yuan, Chenglei Yang, Dongxiao Yu
Format: Article
Language:English
Published: Tsinghua University Press 2024-03-01
Series:Big Data Mining and Analytics
Subjects:
Online Access:https://www.sciopen.com/article/10.26599/BDMA.2023.9020006
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1832568842785652736
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
work_keys_str_mv AT junwang incrementaldatastreamclassificationwithadaptivemultitaskmultiviewlearning
AT maiwangshi incrementaldatastreamclassificationwithadaptivemultitaskmultiviewlearning
AT xiaozhang incrementaldatastreamclassificationwithadaptivemultitaskmultiviewlearning
AT yanli incrementaldatastreamclassificationwithadaptivemultitaskmultiviewlearning
AT yunshengyuan incrementaldatastreamclassificationwithadaptivemultitaskmultiviewlearning
AT chengleiyang incrementaldatastreamclassificationwithadaptivemultitaskmultiviewlearning
AT dongxiaoyu incrementaldatastreamclassificationwithadaptivemultitaskmultiviewlearning