Ensemble multi-label classification using closed frequent labelsets and label taxonomies

Ensembles are computational models that combine the strengths of multiple algorithms or models to enhance predictive accuracy, robustness, and generalization across various applications in machine learning and data analysis. They can mitigate the risk of overfitting and improve model stability, reducing the impact of individual algorithmic biases. These are valuable tools for achieving superior performance in complex and dynamic real-world scenarios. Despite constant advances in this research area, recent studies have shown that state-of-the-art ensembles for multi-label classification are still based on classical ensemble methods from 2016. This study proposes three new ensemble algorithms, called the ensemble of flat-to-hierarchical (EF2H) versions, developed using the F2H multi-label classification model. The F2H algorithm transforms the multi- label problem into a hierarchical multi-label problem to generate predictions. Experiments were conducted with 32 multi-label datasets, and the results were compared with those of the state-of-the-art algorithms in this field. The results demonstrate that the EF2H versions are highly competitive algorithms, outperforming the well-known ensemble of classifier chains (ECC) and achieving predictive performance equivalent to that of the random forest of decision trees with binary relevance (RFDTBR) and random forest of predictive clustering trees (RFPCT) algorithms.