Abstract The random k-labelsets ensemble (RAkEL) is a multi-label learning strategy that integrates many single-label learning models. Each single-label model is constructed using a label powerset (LP) technique based on a randomly generated size-k label subset. Although RAkEL can improve the generalization capability and reduce the complexity of the original LP method, the quality of the randomly generated label subsets could be low. On the one hand, the transformed classes may be difficult to separate in the feature space, negatively affecting the performance; on the other hand, the classes might be highly imbalanced, resulting in difficulties in using the existing single-label algorithms. To solve these problems, we propose an active k-labelsets ensemble (ACkEL) paradigm. Borrowing the idea of active learning, a label-selection criterion is proposed to evaluate the separability and balance level of the classes transformed from a label subset. Subsequently, by randomly selecting the first label or label subset, the remaining ones are iteratively chosen based on the proposed criterion. ACkEL can be realized in both the disjoint and overlapping modes, which adopt pool-based and stream-based frameworks, respectively. Experimental comparisons demonstrate the feasibility and effectiveness of the proposed methods.

中文翻译：

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用于多标签分类的 Active k-Labelsets Ensemble

摘要 随机 k 标签集集成（RAkEL）是一种多标签学习策略，它集成了许多单标签学习模型。每个单标签模型都是使用基于随机生成的大小为 k 的标签子集的标签功率集 (LP) 技术构建的。尽管 RAkEL 可以提高泛化能力并降低原始 LP 方法的复杂度，但随机生成的标签子集的质量可能较低。一方面，转换后的类可能难以在特征空间中分离，对性能产生负面影响；另一方面，类别可能高度不平衡，导致难以使用现有的单标签算法。为了解决这些问题，我们提出了一种主动 k-labelsets ensemble (AckEL) 范式。借用主动学习的思想，提出了一个标签选择标准来评估从标签子集转换而来的类的可分离性和平衡水平。随后，通过随机选择第一个标签或标签子集，根据提出的标准迭代选择剩余的标签。ACKEL 可以在不相交和重叠两种模式下实现，分别采用基于池和基于流的框架。实验比较证明了所提出方法的可行性和有效性。分别采用基于池和基于流的框架。实验比较证明了所提出方法的可行性和有效性。分别采用基于池和基于流的框架。实验比较证明了所提出方法的可行性和有效性。