Active learning for text classification with reusability

Where active learning with uncertainty sampling is used to generate training sets for classification applications, it is sensible to use the same type of classifier to select the most informative training examples as the type of classifier that will be used in the final classification application. There are scenarios, however, where this might not be possible, for example due to computational complexity. Such scenarios give rise to the reusability problem—are the training examples deemed most informative by one classifier type necessarily as informative for a different classifier types? This paper describes a novel exploration of the reusability problem in text classification scenarios. We measure the impact of using different classifier types in the active learning process and in the classification applications that use the results of active learning. We perform experiments on four different text classification problems, using the three classifier types most commonly used for text classification. We find that the reusability problem is a significant issue in text classification; that, if possible, the same classifier type should be used both in the application and during the active learning process; and that, if the ultimate classifier type is unknown, support vector machines should be used in active learning to maximise reusability.