.png){kind=icon}
.png){kind=icon}
Return
An Information-Based Elite-Guided Evolutionary Algorithm for Multi-Objective Feature Selection
.png){kind=icon}
OA
AI
DOI：10.1109/JAS.2023.123810.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Dear Editor, This letter is concerned with the evolution strategy for addressing multi-objective feature selection problems in classification. Previous methods suffer from limitations such as being trapped in local optima and lacking stability. To overcome them, we propose a novel eliteguided mechanism based on information theory. Firstly, an elite solution is generated through a dimension reduction strategy and incorporated to the initialization population. Then, a symmetrical uncertainty-based mutation operator is developed to implement local search after the crossover operator. Finally, a special crowding distance is utilized to analyze duplicates in the environmental selection. The effectiveness and superiority of the proposed method are verified on 20 datasets, including high-dimensional ones.
Keywords:
OPTIMIZATION
Journal
I
IF:
19.2
Papers:
1.4K
Citations:
1.1W