A 2005 paper, Learning Classifiers Using Hierarchically Structured Class Taxonomies, discussed classification into a taxonomy. My general understand is that this problem can be solved by training a set of binary classifiers as the multi-label classification problem. More details delivered by this paper:
- Traditional: classify instances into mutually exclusive class labels,
- Multi-label: an instance may have more than one labels,
- Taxonomic: multi-label and labels are from a hierarchical taxonomy.
- Binarized: train a set of binary classifiers, each for a label in the taxonomy. In classification time, if an instance does not belongs to class C, then no need to check it with classifiers belonging to descendants of C.
- Split-based: need to read more to understand this solution.