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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Large-Margin Label-Calibrated Support Vector Machines for Positive and Unlabeled Learning
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Large-Margin Label-Calibrated Support Vector Machines for Positive and Unlabeled Learning

机译：大幅度标签校准的支持向量机，用于积极和无标签学习

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摘要

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.
机译：积极和无标签学习（PU学习）旨在仅基于PU数据来训练二进制分类器。现有方法通常将PU学习视为标签噪声学习问题或对成本敏感的学习问题。但是，它们在设计模型时都没有充分考虑数据分布信息，这阻碍了它们获得更令人鼓舞的性能。在本文中，我们认为在特征空间中应积极利用正样本和潜在负样本形成的聚类来建立PU学习模型，尤其是当负数据没有明确可用时。为此，我们引入帽子损失来发现数据簇之间的余量，使用标签校准正则器将有偏见的决策边界修改为可能的校正边界，并提出一种新颖的区分性PU分类器，称为“大标签校准支持向量”。机器”（LLSVM）。我们的LLSVM分类器可以在没有负面训练示例的情况下正常工作，并有效地实现正面和负面类别之间的最大余量效果。从理论上讲，我们推导了LLSVM的泛化误差界，这表明PU数据的引入确实有助于提高算法性能。根据经验，我们将LLSVM与最新的PU方法在各种综合和实际数据集上进行了比较，结果证实了所提出的LLSVM在处理PU学习任务方面比其他比较方法更有效。

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