Practical Learning from One-Sided Feedback

In many data mining applications, online labeling feedback is only available for examples which were predicted to belong to the positive class. Such applications include spam filtering in the case where users never check emails marked "spam", document retrieval where users cannot give relevance feedback on unretrieved documents, and online advertising where user behavior cannot be observed for unshown advertisements. One-sided feedback can cripple the performance of classical mistake-driven online learners such as Perceptron. Previous work under the Apple Tasting framework showed how to transform standard online learners into successful learners from one sided feedback. However, we find in practice that this transformation may request more labels than necessary to achieve strong performance. In this paper, we employ two active learning methods which reduce the number of labels requested in practice. One method is the use of Label Efficient active learning. The other method, somewhat surprisingly, is the use of margin-based learners without modification, which we show combines implicit active learning and a greedy strategy to managing the exploration exploitation tradeoff. Experimental results show that these methods can be significantly more effective in practice than those using the Apple Tasting transformation, even on minority class problems.