Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 30, Issue 11, Pages 3471-3483Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2019.2892403
Keywords
Support vector machines; Calibration; Training; Training data; Data models; Learning systems; Intserv networks; Label calibration; large margin; positive and unlabeled learning (PU Learning); support vector machines (SVMs)
Categories
Funding
- NSF of China [61602246, U1713208]
- NSF of Jiangsu Province [BK20171430]
- Fundamental Research Funds for the Central Universities [30918011319]
- State Key Laboratory of Integrated Services Networks, Xidian University [ISN1903]
- 111 Program [AH92005]
- Summit of the Six Top Talents Program [DZXX-027]
- Program for Changjiang Scholars
- Lift Program for Young Talents of Jiangsu Province
- CAST Lift Program for Young Talents
- Australian Research Council [FL-170100117, DE-1901014738, DP-180103424]
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Positive and unlabeled learning (PU learning) aims to train a binary classifier based on only PU data. Existing methods usually cast PU learning as a label noise learning problem or a cost-sensitive learning problem. However, none of them fully take the data distribution information into consideration when designing the model, which hinders them from acquiring more encouraging performance. In this paper, we argue that the clusters formed by positive examples and potential negative examples in the feature space should be critically utilized to establish the PU learning model, especially when the negative data are not explicitly available. To this end, we introduce a hat loss to discover the margin between data clusters, a label calibration regularizer to amend the biased decision boundary to the potentially correct one, and propose a novel discriminative PU classifier termed Large-margin Label-calibrated Support Vector Machines (LLSVM). Our LLSVM classifier can work properly in the absence of negative training examples and effectively achieve the max-margin effect between positive and negative classes. Theoretically, we derived the generalization error bound of LLSVM which reveals that the introduction of PU data does help to enhance the algorithm performance. Empirically, we compared LLSVM with state-of-the-art PU methods on various synthetic and practical data sets, and the results confirm that the proposed LLSVM is more effective than other compared methods on dealing with PU learning tasks.
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