无标签时利用附加信息生成评分，适用于医疗等场景
Learning to Score
- 融合表示学习、附加信息与度量学习构建评分模型
- 在基准数据集和医疗记录上验证了评分有效性
- 适合标签缺失但有相关背景信息的场景
常见机器学习任务从有准确标签的监督学习，到标签稀疏或噪声大的半监督、弱监督学习，再到标签不可得的无监督学习。本文研究目标标签不可用但存在相关附加信息的情况。该附加信息称为侧信息，与未知标签相关或对特征空间施加约束。我们将其建模为表示学习、侧信息与度量学习的集成系统。所提出的评分模型适用于多种场景，例如在医疗领域中，当症状已知但疾病进展标准不明确时，可生成疾病严重程度评分。我们在知名基准数据集和生物医学患者记录上验证了该评分系统的实用性。
原文摘要 · Abstract (English)
Common machine learning settings range from supervised tasks, where accurately labeled data is accessible, through semi-supervised and weakly-supervised tasks, where target labels are scant or noisy, to unsupervised tasks where labels are unobtainable. In this paper we study a scenario where the target labels are not available but additional related information is at hand. This information, referred to as Side Information, is either correlated with the unknown labels or imposes constraints on the feature space. We formulate the problem as an ensemble of three semantic components: representation learning, side information and metric learning. The proposed scoring model is advantageous for multiple use-cases. For example, in the healthcare domain it can be used to create a severity score for diseases where the symptoms are known but the criteria for the disease progression are not well defined. We demonstrate the utility of the suggested scoring system on well-known benchmark data-sets and bio-medical patient records.
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