  Feature selection is a crucial step in data mining to enhance model
performance by reducing data dimensionality. However, the increasing
dimensionality of collected data exacerbates the challenge known as the "curse
of dimensionality", where computation grows exponentially with the number of
dimensions. To tackle this issue, evolutionary computational (EC) approaches
have gained popularity due to their simplicity and applicability.
Unfortunately, the diverse designs of EC methods result in varying abilities to
handle different data, often underutilizing and not sharing information
effectively. In this paper, we propose a novel approach called PSO-based
Multi-task Evolutionary Learning (MEL) that leverages multi-task learning to
address these challenges. By incorporating information sharing between
different feature selection tasks, MEL achieves enhanced learning ability and
efficiency. We evaluate the effectiveness of MEL through extensive experiments
on 22 high-dimensional datasets. Comparing against 24 EC approaches, our method
exhibits strong competitiveness. Additionally, we have open-sourced our code on
GitHub at https://github.com/wangxb96/MEL.
