Hierarchical MultiClass AdaBoost
Charalampos Chelmis, Wenting Qi · 2021 IEEE International Conference on Big Data (Big Data) · 2021
One of the most challenging machine learning problems is a particular case of classification in which classes are hierarchically structured and data instances can be assigned multiple labels residing in a path of the hierarchy. In this paper, we propose hierarchy–aware multiclass AdaBoost, allowing for the first time weak classifiers in an ensemble learning setting to be trained for hierarchical multiclass classification while incorporating a hierarchy–aware loss function directly into the training process. Experimental results on numerous real–world datasets show that, despite its simplicity, the proposed algorithm outperforms all baselines, arising as the state of the art in hierarchical multiclass classification.