Multilabel feature selection is an effective preprocessing step for improving multilabel classification accuracy, because it highlights discriminative features for multiple labels. Recently, multi-population genetic algorithms have gained significant attention with regard to feature selection studies. This is owing to their enhanced search capability when compared to that of traditional genetic algorithms that are based on communication among multiple populations. However, conventional methods employ a simple communication process without adapting it to the multilabel feature selection problem, which results in poor-quality final solutions. In this paper, we propose a new multi-population genetic algorithm, based on a novel communication process, which is specialized for the multilabel feature selection problem. Our experimental results on 17 multilabel datasets demonstrate that the proposed method is superior to other multi-population-based feature selection methods.

中文翻译：

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基于标签互补通信的多标签特征选择的多种群遗传算法

多标签特征选择是提高多标签分类准确性的有效预处理步骤，因为它突出了多个标签的判别特征。最近，多群体遗传算法在特征选择研究方面获得了极大的关注。这是由于与基于多个种群之间通信的传统遗传算法相比，它们增强了搜索能力。然而，传统方法采用简单的通信过程，而没有使其适应多标签特征选择问题，这导致最终解决方案的质量较差。在本文中，我们提出了一种新的多种群遗传算法，它基于一种新的通信过程，专门用于多标签特征选择问题。