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 this https URL

该研究提出了一种名为 PSO-based Multi-task Evolutionary Learning（MEL）的新方法，通过利用多任务学习来提升特征选择的学习能力和效率，并在 22 个高维数据集上进行了广泛实验，与其他 24 种进化计算方法进行对比，表现出强大的竞争力。