Daily unbalanced action recognition based on active learning
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-16181-4.pdf
Reference46 articles.
1. Adaimi R, Thomaz E (2019) Leveraging active learning and conditional mutual information to minimize data annotation in human activity recognition [J]. Proc ACM Interact Mob Wearable Ubiquitous Technol 3(3):1–23. https://doi.org/10.1145/3351228
2. Akbari A, Castilla RS, Jafari R et al (2020) Using intelligent personal annotations to improve human activity recognition for movements in natural environments [J]. IEEE J Biomed Health Inform 24(9):2639–2650. https://doi.org/10.1109/JBHI.2020.2966151
3. Alani AA, Cosma G, Taherkhani A (2020) Classifying imbalanced multi-modal sensor data for human activity recognition in a smart home using deep learning [C]. In: 2020 international joint conference on neural networks (IJCNN). IEEE, pp 1–8. https://doi.org/10.1109/IJCNN48605.2020.9207697
4. Arafat MY, Hoque S, Farid DM (2017) Cluster-based under-sampling with random forest for multi-class imbalanced classification [C]. In: 2017 11th international conference on software, knowledge, information management and applications (SKIMA). IEEE, pp 1–6. https://doi.org/10.1109/SKIMA.2017.8294105
5. Arafat MY, Hoque S, Xu S et al (2019) An under-sampling method with support vectors in multi-class imbalanced data classification [C]. In: 2019 13th international conference on software, knowledge, information management and applications (SKIMA). IEEE, pp 1–6. https://doi.org/10.1109/SKIMA47702.2019.8982391
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