The newly deployed service -- one kind of change service, could lead to a new
type of minority fault. Existing state-of-the-art methods for fault
localization rarely consider the imbalanced fault classification in change
service. This paper proposes a novel method that utilizes decision rule sets to
deal with highly imbalanced data by optimizing the F1 score subject to
cardinality constraints. The proposed method greedily generates the rule with
maximal marginal gain and uses an efficient minorize-maximization (MM) approach
to select rules iteratively, maximizing a non-monotone submodular lower bound.
Compared with existing fault localization algorithms, our algorithm can adapt
to the imbalanced fault scenario of change service, and provide interpretable
fault causes which are easy to understand and verify. Our method can also be
deployed in the online training setting, with only about 15% training overhead
compared to the current SOTA methods. Empirical studies showcase that our
algorithm outperforms existing fault localization algorithms in both accuracy
and model interpretability.

利用决策规则集处理不平衡数据，本文提出了一种新方法，旨在适应改变服务中不平衡故障情景，并提供易于理解和验证的可解释故障原因。与现有的故障定位算法相比，该算法在准确性和模型可解释性方面表现优异。