Showing papers on "Random forest published in 1999"
22 Dec 1999
TL;DR: It is found that Decision Forest outperforms both C4.5 and kNN in all cases, and that category dependent term selection yields better accuracies.
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.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
20 citations
TL;DR: This work proposes to choose the shrinkage parameter for each base tree in bagging by using only extra-bootstrap observations as test cases, and finds that this proposal leads to improved accuracy over that from bagging unshrunken trees.
6 citations