MEL: Efficient Multi-Task Evolutionary Learning for High-Dimensional Feature Selection
Hong Kong Baptist University（香港浸会大学）；Institute of Artificial Intelligence and Future Networks, Beijing Normal University (Zhuhai campus)（北京师范大学（珠海校区）人工智能与未来网络研究院）
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.
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Mehran University of Engineering and Technology（梅赫拉工程技术大学）；ADAPT – Science Foundation Ireland Research Centre（ADAPT——爱尔兰科学基金会研究中心）；Dublin City University（都柏林城市大学）；Lero – the Irish Software Research Centre（Lero——爱尔兰软件研究中心）；National University of Ireland, Galway（爱尔兰国立高威大学）
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