论文标题
多标签分类的跨符号预测指标
A Cross-Conformal Predictor for Multi-label Classification
论文作者
论文摘要
与每个实例与单个类关联的典型分类设置不同，在多标签学习中，每个实例都会同时与多个类关联。因此，在此设置中的学习任务是预测每个实例所属的类的子集。这项工作研究了一个新近开发的称为共形预测（CP）到多标签学习设置的框架的应用。 CP通过可靠的置信度来补充机器学习算法的预测。结果，提出的方法不仅是预测新的看不见实例最可能的类的子集，还表明每个预测子集都是正确的可能性。在多标签环境中，这些附加信息尤其有价值，在多标签环境中，整体不确定性非常高。
Unlike the typical classification setting where each instance is associated with a single class, in multi-label learning each instance is associated with multiple classes simultaneously. Therefore the learning task in this setting is to predict the subset of classes to which each instance belongs. This work examines the application of a recently developed framework called Conformal Prediction (CP) to the multi-label learning setting. CP complements the predictions of machine learning algorithms with reliable measures of confidence. As a result the proposed approach instead of just predicting the most likely subset of classes for a new unseen instance, also indicates the likelihood of each predicted subset being correct. This additional information is especially valuable in the multi-label setting where the overall uncertainty is extremely high.
