.png){kind=icon}
.png){kind=icon}
Return
Enhancing evolutionary multitasking for high-dimensional feature selection through task relevance evaluation and knowledge transfer
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2025.114076.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• Present a new multi-task feature selection framework for high-dimensional data. • Introduce a novel multi-task generation strategy through task relevance evaluation. • Enhance multi-task optimization algorithm with a new knowledge transfer strategy. • Perform extensive simulations to validate the effectiveness of the proposed method. • Explore and determine the optimal task-crossing ratio through simulations.
Keywords:
multi-task learning
feature selection
high-dimensional data
knowledge transfer
task relevance