{"type":"ScholarlyArticle","title":"DWLR: Domain Adaptation under Label Shift for Wearable Sensor","authors":[{"name":"Juren Li","slug":"juren-li","given":"Juren","family":"Li","affiliation":"College of Computer Science and Technology , Zhejiang University"},{"name":"Yang Yang","slug":"yang-yang","given":"Yang","family":"Yang","affiliation":"College of Computer Science and Technology , Zhejiang University"},{"name":"Youmin Chen","slug":"youmin-chen","given":"Youmin","family":"Chen","affiliation":"College of Computer Science and Technology , Zhejiang University"},{"name":"Jianfeng Zhang","slug":"jianfeng-zhang","given":"Jianfeng","family":"Zhang","affiliation":"Huawei Noah's Ark Lab {jrlee ,"},{"name":"Zeyu Lai","slug":"zeyu-lai","given":"Zeyu","family":"Lai","affiliation":"College of Computer Science and Technology , Zhejiang University"},{"name":"Lujia Pan","slug":"lujia-pan","given":"Lujia","family":"Pan","affiliation":"Huawei Noah's Ark Lab {jrlee ,"}],"abstract":"Wearable sensors play a crucial role in real-world scenarios, such as human activity recognition, sleep monitoring and electrocardiogram monitoring. However, deploying classifiers on them is challenged by distribution shifts across users and devices. Unsupervised domain adaptation (UDA) is proposed to address this, yet existing methods mostly focus on feature distribution shift, neglecting the potential misclassification due to label shift. In this paper, we propose Domain adaptation under label shift for Wearable sensor with Learnable Reweighting (DWLR) to handle both feature and label shifts. Specifically, DWLR employs learnable reweighting to align label distributions between source and target domains. It incorporates elements of information gain during the reweighting process to counter potential distribution shift that could emerge from over-reliance on data with high-confidence pseudo labels. Importantly, since wearable sensor data is time-series data,  and can be subjected to distribution shifts originating from either the time domain, the frequency domain, or both, DWLR performs reweighting and alignment separately in these two domains to more robustly handle potential feature distribution shifts. Extensive experiments on three distinct wearable sensor datasets demonstrate the effectiveness of DWLR, yielding a remarkable average performance improvement of 5.85%.","doi":"10.24963/ijcai.2024/489","url":"https://ijcai.org/proceedings/2024/489","pdf":"https://ijcai.org/proceedings/2024/0489.pdf","markdown":"https://ijcai.org/proceedings/2024/489.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/489","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":489,"pages":{"first":4425,"last":4433},"keywords":["Machine Learning: ML: Time series and data streams","Machine Learning: ML: Adversarial machine learning","Machine Learning: ML: Classification","Machine Learning: ML: Deep learning architectures"],"videos":[],"references":[{"position":1,"raw":"[Anguita et al., 2013] Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra Perez, and Jorge Luis Reyes Or- tiz. 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