Evaluation of decision forests on text categorization

Hao Chen; Tin Kam Ho

doi:10.1117/12.373494

22 December 1999 Evaluation of decision forests on text categorization

Hao Chen, Tin Kam Ho

Proceedings Volume 3967, Document Recognition and Retrieval VII; (1999) https://doi.org/10.1117/12.373494
Event: Electronic Imaging, 2000, San Jose, CA, United States

Abstract

Text categorization is useful for indexing documents for information retrieval, filtering parts for document understanding, and summarizing contents of documents of special interests. We describe a text categorization task and an experiment using documents from the Reuters and OHSUMED collections. We applied the Decision Forest classifier and compared its accuracies to those of C4.5 and kNN classifiers 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 al three classifiers degrade from the Reuters collection to the OHSUMED collection, but Decision Forest remains to be superior.

Citation Download Citation

Hao Chen and Tin Kam Ho "Evaluation of decision forests on text categorization", Proc. SPIE 3967, Document Recognition and Retrieval VII, (22 December 1999); https://doi.org/10.1117/12.373494

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