Evaluation of decision forests on text categorization

Text categorization is useful for indexing documents for information retrieval, ltering parts for document understanding, and summarizing contents of documents of special interests. We describe a text categoriza-tion task and an experiment using documents from the Reuters and OHSUMED collections. We applied the Decision Forest classiier and compared its accuracies to those of C4.5 and kNN classiiers, using both category dependent and category independent term selection schemes. It is found that Decision Forest outperforms both C4.5 and kNN in all cases, and that category dependent term selection yields better accuracies. Performances of all three classiiers degrade from the Reuters collection to the OHSUMED collection, but Decision Forest remains to be superior.