Maximizing text-mining performance
- 1 July 1999
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Intelligent Systems and their Applications
- Vol. 14 (4) , 63-69
- https://doi.org/10.1109/5254.784086
Abstract
The authors' adaptive resampling approach surpasses previous decision-tree performance and validates the effectiveness of small, pooled local dictionaries. They demonstrate their approach using the Reuters-21578 benchmark data and a real-world customer E-mail routing system.Keywords
This publication has 7 references indexed in Scilit:
- An Evaluation of Statistical Approaches to Text CategorizationInformation Retrieval Journal, 1999
- Inductive learning algorithms and representations for text categorizationPublished by Association for Computing Machinery (ACM) ,1998
- Boosting and Rocchio applied to text filteringPublished by Association for Computing Machinery (ACM) ,1998
- Data mining with decision trees and decision rulesFuture Generation Computer Systems, 1997
- Bagging predictorsMachine Learning, 1996
- Automated learning of decision rules for text categorizationACM Transactions on Information Systems, 1994
- Feature selection and feature extraction for text categorizationPublished by Association for Computational Linguistics (ACL) ,1992