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Abstract:
This paper addresses the problem of semi-supervised classification on
document collections using retraining (also called self-training). A
possible application is focused Web
crawling which may start with very few, manually selected, training
documents
but can be enhanced by automatically adding initially unlabeled,
positively classified Web pages for retraining.
Such an approach is by itself not robust and faces tuning problems
regarding parameters
like the number of selected documents, the number of retraining
iterations, and the ratio of positive
and negative classified samples used for retraining.
The paper develops methods for automatically tuning these parameters,
based on
predicting the leave-one-out error for a re-trained classifier and
avoiding that the classifier is diluted by selecting too many or weak
documents for retraining.
Our experiments
with three different datasets
confirm the practical viability of the approach.
