.png){kind=icon}
.png){kind=icon}
Return
Incremental deep forest for multi-label data streams learning
.png){kind=icon}
PRE
AI
DOI：10.1007/s10489-022-03414-6.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Multi-label learning has attracted many attentions. However, the continuous data generated in the fields of sensors, network access, etc., that is data streams, the scenario brings challenges such as real-time, limited memory, once pass. Several learning algorithms have been proposed for offline multi-label classification, but few researches develop it for dynamic multi-label incremental learning models based on cascading schemes. Deep forest can perform representation learning layer by layer, and does not rely on backpropagation, using this cascading scheme, this paper proposes a multi-label data stream deep forest (VDSDF) learning algorithm based on cascaded Very Fast Decision Tree (VFDT) forest, which can receive examples successively, perform incremental learning, and adapt to concept drift. Experimental results show that the proposed VDSDF algorithm, as an incremental classification algorithm, is more competitive than batch classification algorithms on multiple indicators. Moreover, in dynamic flow scenarios, the adaptability of VDSDF to concept drift is better than that of the contrast algorithm.
Keywords:
Multi-label
Classification
Incremental learning
Streaming scene
Deep forest
Journal
IF:
3.5
Papers:
7.5K
Citations:
1.7W