.png){kind=icon}
返回
Data stream classification using a deep transfer learning method based on extreme learning machine and recurrent neural network
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-023-18075-x.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Deep learning-based approaches have gained popularity for many applications in recent years and have become the state-of-the-art method in machine learning applications. However, most deep learning applications manipulate static data sets that do not vary over time. This is not the case for several significant recent applications, particularly for online learning on data streams that require real-time adjustment to the latest data context. This paper presents an architecture derived from the theory of transfer learning utilizing Extreme Learning Machine (ELM) and Recurrent Neural Network (RNN) methods. Unlike most data stream applications, when receiving high volumes of data at a high velocity, the proposed method does not store and reuse previous observations. The proposed model uses a Deep Echo State Network (DeepESN) for the primary classification task to compensate for the lack of past data. More precisely, instead of the data itself, we propagate generative models so that historical training data that is not stored can be regenerated. Before the DeepESN block, a layer of pre-trained ELMs was used to have generic feature engineering. The proposed method was tested on well-known benchmark data sets, and it outperformed the state-of-the-art models by an accuracy improvement ranging from 0.02 to 3.3 percent. The impact of data set regeneration on the learning process was studied using different RNN approaches.
Keyword:
Data streams
Deep learning
Adaptive learning
Transfer learning
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
机构
引用论文
A Deep Long Short-Term Memory based classifier for Wireless Intrusion Detection System基于深度长短期记忆的无线入侵检测系统分类器.png)
ICT EXPRESS
OAAI
ICT EXPRESSIF4.2
OAAI分享收藏分享收藏
Data Stream Classification Based on Extreme Learning Machine: Review基于极限学习机的数据流分类研究综述BIG DATA RESEARCH
BIG DATA RESEARCHIF4.2
PREAI
分享收藏Managed honeybee colony losses of the Eastern honeybee (Apis cerana) in China (2011–2014)ApidologieOAAI
ApidologieIF0
OAAI分享收藏
查看更多内容