Multi-label learning is a popular area of machine learning research as it is widely applicable to many real-world scenarios. In comparison with traditional binary and multi-classification tasks, the multi-label data are more easily impacted or destroyed by an imbalanced data distribution. This paper describes an adaptive decision threshold-based extreme learning machine algorithm (ADT-ELM) that addresses the imbalanced multi-label data classification problem. Specifically, the macro and micro F-measure metrics are adopted as the optimization functions for ADT-ELM, and the particle swarm optimization algorithm is employed to determine the optimal decision threshold combination. We use the optimized thresholds to make decision for future multi-label instances. Twelve baseline multi-label data sets are used in a series of experiments o verify the effectiveness and superiority of the proposed algorithm. The experimental results indicate that the proposed ADT-ELM algorithm is significantly superior to many state-of-the-art multi-label imbalance learning algorithms, and it generally requires less training time than more sophisticated algorithms.

多标签学习是机器学习研究的一个热门领域，因为它广泛适用于许多现实情况。与传统的二进制和多分类任务相比，多标签数据更容易受到不平衡数据分布的影响或破坏。本文介绍了一种基于自适应决策阈值的极限学习机算法（ADT-ELM），该算法解决了不平衡的多标签数据分类问题。具体而言，采用宏观和微观F度量指标作为ADT-ELM的优化函数，并采用粒子群优化算法确定最佳决策阈值组合。我们使用优化的阈值为将来的多标签实例做出决策。在一系列实验中使用了十二个基线多标签数据集，以验证所提出算法的有效性和优越性。实验结果表明，所提出的ADT-ELM算法明显优于许多最新的多标签不平衡学习算法，并且与更复杂的算法相比，通常所需的训练时间更少。