Many real-world classification problems are becoming multi-label in nature, i.e., multiple class labels are assigned to an instance simultaneously. Multi-label classification is a challenging problem due to the involvement of three forms of interactions, i.e., feature-to-feature, feature-to-label, and label-to-label interactions. What further complicates the problem is that not all features are useful, and some can deteriorate the classification performance. Sparsity-based methods have been widely used to address multi-label feature selection due to their efficiency and effectiveness. However, most (if not all) existing methods do not consider the three forms of interactions simultaneously, which could hinder their ability to achieve good performance. Moreover, most existing methods are gradient-based, which are prone to getting stuck at local optima. This paper proposes a new sparsity-based feature selection approach that can simultaneously consider all three forms of interactions. Furthermore, this paper develops a novel sparse learning method based on particle swarm optimisation that can avoid local optima. The proposed method is compared against the state-of-the-art multi-label feature selection methods in terms of multi-label classification performance. The results show that our method performed significantly better in selecting high-quality feature subsets with respect to various feature subset sizes.

中文翻译：

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基于粒子群优化的多标签特征选择的双稀疏结构化子空间和图正则化


许多现实世界的分类问题本质上正在变得多标签，即多个类标签同时分配给一个实例。由于涉及三种形式的交互，即特征到特征、特征到标签和标签到标签交互，多标签分类是一个具有挑战性的问题。使问题进一步复杂化的是，并非所有特征都是有用的，有些特征可能会降低分类性能。基于稀疏性的方法由于其效率和有效性而被广泛用于解决多标签特征选择问题。然而，大多数（如果不是全部）现有方法不会同时考虑三种形式的交互，这可能会阻碍它们实现良好性能的能力。此外，大多数现有方法都是基于梯度的，很容易陷入局部最优。本文提出了一种新的基于稀疏性的特征选择方法，可以同时考虑所有三种形式的交互。此外，本文提出了一种基于粒子群优化的新颖稀疏学习方法，可以避免局部最优。所提出的方法在多标签分类性能方面与最先进的多标签特征选择方法进行了比较。结果表明，我们的方法在选择各种特征子集大小的高质量特征子集方面表现明显更好。