Active Learning from Positive and Unlabeled Data

TitleActive Learning from Positive and Unlabeled Data
Publication TypeConference Paper
Year of Publication2011
AuthorsGhasemi, A., H. R. Rabiee, M. Fadaee, M. T. Manzouri, and M. H. Rohban
Conference NameICDM 2011 Workshop on Optimization Based Methods for Emerging Data Mining Problems
Date Published12/2011
Publisher2011
Conference LocationVancouver, Canada
Keywordsactive learning, learning from positive and unlabelled data, one-class learning, Semi-Supervised Learning, uncertainty sampling
Abstract uring recent years, active learning has evolved into a popular paradigm for utilizing user’s feedback to improve accuracy of learning algorithms. Active learning works by selecting the most informative sample among unlabeled data and querying the label of that point from user. Many different methods such as uncertainty sampling and minimum risk sampling have been utilized to select the most informative sample in active learning. However, although many active learning algorithms have been proposed so far, most of them work with binary or multi-class classification problems and therefore can not be applied to problems in which only samples from one class as well as a set of unlabeled data are available. Such problems arise in many real-world situations and are knows as the problem of learning from positive and unlabeled data. In this paper we propose an active learning algorithm that can work when only samples of one class as well as a set of unlabelled data are available. Experiments and empirical analysis show promising results compared to other similar methods.
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