Loading...
Thumbnail Image
Item

A novel two stage scheme utilizing the test set for model selection in text classification

Abstract
Text classification is a natural application domain for semi-supervised learning, as labeling documents is expensive, but on the other hand usually an abundance of unlabeled documents is available. We describe a novel simple two stage scheme based on dagging which allows for utilizing the test set in model selection. The dagging ensemble can also be used by itself instead of the original classifier. We evaluate the performance of a meta classifier choosing between various base learners and their respective dagging ensembles. The selection process seems to perform robustly especially for small percentages of available labels for training.
Type
Conference Contribution
Type of thesis
Series
Citation
Pfahringer, B., Reutemann, P., Mayo, M. (2005). A novel two stage scheme utilizing the test set for model selection in text classification. Paper presented at the 18th Australian Joint Conference on Artificial Intelligence, University of Technology, Sydney, Australia, December 5-9, 2005.
Date
2005
Publisher
University of Technology, Sydney
Degree
Supervisors
Rights