Types of Classification:
- 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.
Solutions Proposed:
- 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.
From the experiment results, it seems that above two solutions have similar performance. And both differs from the bottom-up solution that I saw in Google.
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