论文标题
多标签问题的怀疑二进制推论与概率集
Skeptical binary inferences in multi-label problems with sets of probabilities
论文作者
论文摘要
在本文中，我们考虑了对多标签问题或布尔矢量提出的分布稳健，持怀疑态度的推论的问题。通过分配稳健，我们的意思是我们考虑一组可能的概率分布，并且通过怀疑，我们知道我们将其视为有效的仅对于本集中每个分布的正确推论。只要考虑到足够大的集合，这种推论就会提供部分预测。我们特别研究了锤损失案例，这是多标签问题中的共同损失函数，显示了如何在这种情况下进行怀疑的推论。我们的实验结果分为三个部分。 （1）第一个指示通过使用综合数据集从我们的理论结果中获得的收益，（2）第二个表明我们的方法在那些难以预测的实例上会产生相关的效率规则。
In this paper, we consider the problem of making distributionally robust, skeptical inferences for the multi-label problem, or more generally for Boolean vectors. By distributionally robust, we mean that we consider a set of possible probability distributions, and by skeptical we understand that we consider as valid only those inferences that are true for every distribution within this set. Such inferences will provide partial predictions whenever the considered set is sufficiently big. We study in particular the Hamming loss case, a common loss function in multi-label problems, showing how skeptical inferences can be made in this setting. Our experimental results are organised in three sections; (1) the first one indicates the gain computational obtained from our theoretical results by using synthetical data sets, (2) the second one indicates that our approaches produce relevant cautiousness on those hard-to-predict instances where its precise counterpart fails, and (3) the last one demonstrates experimentally how our approach copes with imperfect information (generated by a downsampling procedure) better than the partial abstention [31] and the rejection rules.
