多居民活动认可中的阶级不平衡:对深度学习方法的解释性进行评估研究
Deepika Singh1,2, Erinc Merdivan2, Johannes Kropf2
1Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Graz, Austria.
Universal access in the information society
|June 20, 2025
概括
本研究使用深度学习来解决多居民活动识别中的阶级不平衡问题. 长期短期记忆和双向长期短期记忆网络显示出对可靠系统的承诺.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 由于行动分布不均,在多居民家庭中对活动的认可具有挑战性.
- 深度学习模型在多人场景中与传感器数据中的阶级不平衡作斗争.
- 现有的方法往往侧重于单个居民或平衡数据集.
研究的目的:
- 调查多居民活动认可中的阶级不平衡问题.
- 评估长期短期内存 (LSTM) 和双向长期短期内存 (BiLSTM) 网络.
- 调查数据层面和算法策略,以减轻类不平衡并提高模型可解释性.
主要方法:
- 关于活动认可中的阶级不平衡的综合文献调查.
- 在不平衡的智能家居数据集上对LSTM和BiLSTM网络进行实验性评估.
- 应用数据级和算法技术来解决阶级不平衡.
- 使用各种指标分析模型性能和可解释性.
主要成果:
- 深度学习模型,特别是LSTM和BiLSTM,尽管存在阶级不平衡,但可以有效地识别多居民活动.
- 数据级和算法策略显著提高了对不平衡数据集的模型性能.
- 研究的策略提高了活动识别系统的透明度和可靠性.
结论:
- 解决阶级不平衡对于准确识别多居民活动至关重要.
- 随着适当的不平衡处理,LSTM和BiLSTM网络为主动和辅助生活技术提供了可行的解决方案.
- 这项研究有助于为智能家居环境开发更可靠,更易于解释的AI系统.
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