@inproceedings{768,
  keywords = {Hierarchical classification, online learning, learning with constrains},
  author = {Wenting Qi and Charalampos Chelmis},
  title = {Online Hierarchical Multi-label Classification},
  abstract = {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.},
  year = {2023},
  chapter = {5346},
  pages = {10},
  month = {12},
  publisher = {IEEE International Conference on Big Data},
  isbn = {979-8-3503-2445-7},
  url = {https://par.nsf.gov/biblio/10507193},
  doi = {10.1109/BigData59044.2023.10386110},
}