Online Asymmetric Active Learning with Imbalanced Data
Xiaoxuan Zhang*, University of Iowa; Tianbao Yang, Univ of Iowa; Padmini Srinivasan, University of Iowa
This paper considers online learning with imbalanced streaming data under a query budget, where the act of querying for labels is constrained to a budget limit. We study different active querying strategies for classiﬁcation. In particular, we propose an asymmetric active querying strategy that assigns diﬀerent probabilities for query to examples predicted as positive and negative. To corroborate the proposed asymmetric query model, we provide a theoretical analysis on a weighted mistake bound. We conduct extensive evaluations of the proposed asymmetric active querying strategy in comparison with several baseline querying strategies and with previous online learning algorithms for imbalanced data. In particular, we perform two types of evaluations ac-cording to which examples appear as “positive”/ “negative”. In push evaluation only the positive predictions given to the user are taken into account; in push and query evaluation the decision to query is also considered for evaluation. The push and query evaluation strategy is particularly suited for a recommendation setting because the items selected for querying for labels may go to the end-user to enable customization and personalization. These would not be shown any diﬀerently to the end-user compared to recommended content (i.e., the examples predicated as positive). Additionally, given our interest in imbalanced data we measure F -score instead of accuracy that is traditionally considered by online classiﬁcation algorithms. We also compare the querying strategies on ﬁve classiﬁcation tasks from diﬀerent domains, and show that the probabilistic query strategy achieves higher F -scores on both types of evaluation than deterministic strategy, especially when the budget is small, and the asymmetric query model further improves performance. When compared to the state-of-the-art cost-sensitive online learning algorithm under a budget, our online classiﬁcation algorithm with asymmetric querying achieves a higher F -score on four of the ﬁve tasks, especially on the push evaluation.
Filed under: Big Data | Time Series and Stream Mining