Hierarchical Classification

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:

Types of Classification:
  1. Traditional: classify instances into mutually exclusive class labels,
  2. Multi-label: an instance may have more than one labels,
  3. Taxonomic: multi-label and labels are from a hierarchical taxonomy.
Solutions Proposed:
  1. 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.
  2. 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.