.png){kind=icon}
返回
Disagreement-based class incremental random forest for sensor-based activity recognition
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2021.108044.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Activity recognition plays a key role in many fields, such as health monitoring and elderly care. Handling changes in user habits is a significant technical challenge in activity recognition. Ideally, a model should adapt to newly emerging classes and concept drift dynamically. This paper proposes a novel semi-supervised class incremental learning method, namely, disagreement-based class incremental random forest (Di-CIRF). The proposed model can detect newly emerging classes and update a previously established activity recognition model through streaming data. First, it is necessary to identify novel candidates by employing the disagreement-based confidence voting mechanism and minimum bounding box (MBB)-based separation detection to annotate newly emerging data accurately. Then, the coarse coding-based cohesion detection strategy is adopted to filter out the true novelty instances. This paper also proposes the iterative MBB-based splitting strategy and the pseudo-instance generation mechanism in Di-CIRF for updating the activity model without retaining the trained data. According to experimental results on four public activity recognition datasets, Di-CIRF outperforms the state-of-the-art methods.
Keyword:
Class incremental learning
Activity recognition
Random forest
Semi-supervised learning
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
分享收藏Classification Under Streaming Emerging New Classes: A Solution Using Completely-Random Trees流新兴类别下的分类: 使用完全随机树的解决方案
OAAI
OAAI分享收藏Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints
PREAI
分享收藏Combining emerging patterns with random forest for complex activity recognition in smart homes将新兴模式与随机森林相结合用于智能家居中的复杂活动识别.png)
APPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
分享收藏分享收藏
查看更多内容