Dual Teaching: A Practical Semi-supervised Wrapper Method
This addresses the challenge of building supervised classifiers from partially labeled data for practical applications, though it appears incremental as it builds on existing wrapper methods.
The paper tackles the problem of applying semi-supervised wrapper methods in practice by proposing Dual Teaching, a method that uses two external classifiers to estimate false positives and false negatives, achieving effectiveness comparable to classifiers trained on fully labeled data under specific conditions.
Semi-supervised wrapper methods are concerned with building effective supervised classifiers from partially labeled data. Though previous works have succeeded in some fields, it is still difficult to apply semi-supervised wrapper methods to practice because the assumptions those methods rely on tend to be unrealistic in practice. For practical use, this paper proposes a novel semi-supervised wrapper method, Dual Teaching, whose assumptions are easy to set up. Dual Teaching adopts two external classifiers to estimate the false positives and false negatives of the base learner. Only if the recall of every external classifier is greater than zero and the sum of the precision is greater than one, Dual Teaching will train a base learner from partially labeled data as effectively as the fully-labeled-data-trained classifier. The effectiveness of Dual Teaching is proved in both theory and practice.