Towards Enabling Binary Decomposition for Partial Label Learning
Towards Enabling Binary Decomposition for Partial Label Learning
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DOI：
10.24963/ijcai.2018/398
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
X. Wu;Min-Ling Zhang
中科院分区：
文献类型：
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作者：
X. Wu;Min-Ling Zhang
部分标签（PL）学习的任务是从训练样本中学习多类分类器，每个训练样本与一组候选标签相关联，其中只有一个对应于真实标签。众所周知，为了引入多类预测模型，最直接的解决方案是二元分解，它通过一对一或一对一策略进行工作。尽管如此，每个 PL 训练示例的真实标签隐藏在其候选标签集中，因此学习算法无法访问，二元分解不能直接应用于部分标签学习场景。在本文中，提出了一种通过采用流行的一对一分解策略来解决部分标签学习问题的新方法。具体来说，为每一对类标签导出一个二元分类器，其中与标签对具有明显相关性的 PL 训练示例用于生成相应的二元训练集。之后，通过叠加现有二元分类器的预测来进一步为每个类标签派生一个二元分类器，以提高泛化能力。对人工和现实世界 PL 数据集的实验研究清楚地验证了所提出的二进制分解方法与最先进的部分标签学习技术的有效性。
The task of partial label (PL) learning is to learn a multi-class classifier from training examples each associated with a set of candidate labels, among which only one corresponds to the ground-truth label. It is well known that for inducing multi-class predictive model, the most straightforward solution is binary decomposition which works by either one-vs-rest or one-vs-one strategy. Nonetheless, the ground-truth label for each PL training example is concealed in its candidate label set and thus not accessible to the learning algorithm, binary decomposition cannot be directly applied under partial label learning scenario. In this paper, a novel approach is proposed to solving partial label learning problem by adapting the popular one-vs-one decomposition strategy. Specifically, one binary classifier is derived for each pair of class labels, where PL training examples with distinct relevancy to the label pair are used to generate the corresponding binary training set. After that, one binary classifier is further derived for each class label by stacking over predictions of existing binary classifiers to improve generalization. Experimental studies on both artificial and real-world PL data sets clearly validate the effectiveness of the proposed binary decomposition approach w.r.t state-of-the-art partial label learning techniques.