计算机科学
人工智能
机器学习
托普西斯
域适应
领域(数学分析)
活动识别
领域知识
数据挖掘
模式识别(心理学)
数学
分类器(UML)
数学分析
运筹学
作者
Yilin Dong,Xinde Li,Jean Dezert,Ri-Gui Zhou,Changming Zhu,Lei Cao,Wei Wang,Shuzhi Sam Ge
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2023-04-01
卷期号:19 (4): 5530-5542
被引量:7
标识
DOI:10.1109/tii.2022.3182780
摘要
In recent years, wearable sensor-based human activity recognition (HAR) is becoming more and more attractive, especially in health monitoring and sports management. However, in order to obtain high-quality HAR, it is often necessary to get sufficient labeled activity data, which is very difficult, time-consuming, and costly in a natural environment. To tackle this problem, multisource domain adaptation (DA) is a promising method that aims to learn enough multisource prior knowledge from labeled activity data, and then transfer this learned knowledge to the target unlabeled dataset. Thus, this article presents a novel multisource weighted DA with evidential reasoning (w-MSDAER) for HAR, which can effectively utilize complementary knowledge between multiple sources. Specifically, we first use the strategy of distribution alignment to learn local domain-invariant classifiers based on multisource domains. And then the reliabilities of these derived classifiers are comprehensively evaluated according to the belief function based technique for order preference by similarity to ideal solution (BF-TOPSIS). Finally, the discounting fusion method is used to fuse the local classification results. Comprehensive experiments are conducted on two open-source datasets, and the results show that the proposed w-MSDAER significantly outperforms other state-of-art methods.
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