We present two different strategies to extend the classical multi-label chaining approach to handle imprecise probability estimates. These estimates use convex sets of distributions (or credal sets) in order to describe our uncertainty rather than a precise one. The main reasons one could have for using such estimations are (1) to make cautious predictions (or no decision at all) when a high uncertainty is detected in the chaining and (2) to make better precise predictions by avoiding biases caused in early decisions in the chaining. Through the use of the naive credal classifier, we propose efficient procedures with theoretical justifications to solve both strategies. Our experimental results on missing labels, which investigate how reliable these predictions are in both approaches, indicate that our approaches produce relevant cautiousness on those hard-to-predict instances where the precise models fail.

翻译：我们提出了两种不同的战略,以扩大传统的多标签链办法,处理不精确的概率估计。这些估计数使用分布的组合(或信条套)来描述我们的不确定性,而不是精确的。 使用这种估计的主要理由可能是:(1) 当链条中发现高度不确定性时作出谨慎的预测(或根本没有任何决定),(2) 通过避免链条早期决定中出现的偏见来作出更准确的预测。 通过使用天真的钉子分类器,我们提出了具有理论理由的高效程序来解决这两种战略。 我们在缺失标签上的实验结果调查了这两种方法中这些预测的可靠性,表明我们的方法在精确模型失败的难以预测的情况下产生了相关的谨慎性。