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Active learning from positive and unlabeled data

Ghasemi, A ; Sharif University of Technology | 2011

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  1. Type of Document: Article
  2. DOI: 10.1109/ICDMW.2011.20
  3. Publisher: 2011
  4. Abstract:
  5. During 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. 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 known 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 unlabeled data are available. Our method works by separately estimating probability density of positive and unlabeled points and then computing expected value of informativeness to get rid of a hyper-parameter and have a better measure of informativeness. Experiments and empirical analysis show promising results compared to other similar methods
  6. Keywords:
  7. Learning from positive and unlabeled data ; Uncertainty sampling ; Active Learning ; Active-learning algorithm ; Empirical analysis ; Expected values ; Hyper-parameter ; Informativeness ; Multiclass classification problems ; One-class learning ; Probability densities ; Real world situations ; Semi-supervised learning ; Unlabeled data ; Data mining ; Learning algorithms
  8. Source: Proceedings - IEEE International Conference on Data Mining, ICDM, 11 December 2011 through 11 December 2011 ; December , 2011 , Pages 244-250 ; 15504786 (ISSN) ; 9780769544090 (ISBN)
  9. URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6137386