首页|基于平衡采样的主动半监督学习人类活动识别研究

基于平衡采样的主动半监督学习人类活动识别研究

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基于可穿戴传感器的人类活动识别研究逐渐受到人们的广泛关注.本文提出了一种基于平衡采样的主动半监督模型,在挑选样本进行标注时,将样本的不确定性和多样性一并考虑在内,挑选出类别平衡的不确定性样本.确保训练后的模型对每个类都有很好的识别性能,从而提升整体分类结果.同时,为了全部利用标记和未标记样本的信息,将主动学习和半监督学习相结合,利用损失项信息不断更新网络参数,提升模型在低注释下的识别性能.该模型在2个公开数据集上得到了验证,在确保获得较优分类准确率的同时,可以大大减少样本的人工标注工作.
Research on active semi-supervised learning for human activity recognition based on balanced sampling
Human activity recognition research based on wearable sensors has gradually attracts widespread attention. An active semi-supervised model based on balanced sampling is proposed. When selecting samples for labeling,the uncertainty and diversity of the samples are taken into account,and the uncertain samples with balanced categories are selected. Ensure that the trained model has good recognition performance for each class, thereby improve the overall classification results. At the same time,in order to fully utilize the information of labeled and unlabeled samples,active learning and semi-supervised learning are combined,the network parameters are continuously updated by using the loss item information to improve the recognition performance of the model under low annotation. The model has been verified on two public datasets,which can greatly reduce the manual labeling work of samples while ensuring better classification accuracy.

active learningsemi-supervised learningquery strategieshuman activity recognition

郇战、刘艳、李志新、董晨辉、周帮文、秦王盛

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常州大学微电子与控制工程学院,江苏 常州 213000

常州大学计算机与人工智能学院阿里云大数据学院,江苏 常州 213000

主动学习 半监督学习 查询策略 人类活动识别

国家自然科学基金面上资助项目国家自然科学基金资助项目

6197602861772248

2024

传感器与微系统
中国电子科技集团公司第四十九研究所

传感器与微系统

CSTPCD北大核心
影响因子:0.61
ISSN:1000-9787
年,卷(期):2024.43(7)
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