Online Hierarchical Multi–label Classification

Existing approaches for multi–label classification are trained offline, missing the opportunity to adapt to new data instances as they become available. To address this gap, an online multi–label classification method was proposed recently, to learn from data instances sequentially. In this work, we focus on multi–label classification tasks, in which the labels are organized in a hierarchy. We formulate online hierarchical multi–labeled classification as an online optimization task that jointly learns individual label predictors and a label threshold, and propose a novel hierarchy constraint to penalize predictions that are inconsistent with the label hierarchy structure. Experimental results on three benchmark datasets show that the proposed approach outperforms online multi–label classification methods, and achieves comparable to, or even better performance than offline hierarchical classification frameworks with respect to hierarchical evaluation metrics.