RAkEL

RAkEL is an ensemble algorithm that breaks complex multi-label classification tasks into manageable, random subsets of labels to capture hidden correlations.

The Random k-Labelsets (RAkEL) algorithm solves the high-dimensional challenges of multi-label learning by partitioning the label space into small, random subsets of size k. Instead of attempting to model every possible label combination at once (a task that often leads to data sparsity and computational bottlenecks), RAkEL trains an ensemble of Label Powerset (LP) classifiers on these overlapping subsets. This approach effectively captures inter-label dependencies while maintaining a manageable number of classes per model. By aggregating the votes from these diverse classifiers, the system provides a robust final prediction that outperforms traditional binary relevance methods in domains like protein function prediction and document tagging.

https://github.com/scikit-multilearn/scikit-multilearn