A Novel Two Stage Scheme Utilizing the Test Set for Model Selection in Text Classification
نویسندگان
چکیده
Text classification is a natural application domain for semisupervised learning, as labeling documents is expensive, but on the other hand usually an abundance of unlabeled documents is available. We describe a novel simple twostage 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.
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