Experiments with multi-label text classifier on the Reuters collection

Text categorization is the classification to assign a text document to an appropriate category in a predefined set of categories. We present an approach on hierarchical text categorization that is a recently emerged subfield of the main topic. Here, documents are assigned to leaf-level categories of a category tree (called taxonomy). The algorithm applies an iterative learning module that allow of gradually creating a classifier by weight adjusting method. We experimented on the well-known Reuters-21578 document corpus with different taxonomies. Results show that our approach outperforms existing ones by up to 10%.