In real-world microservice systems, the newly deployed service - one kind of change service, could lead to a new type of minority fault. Existing state-of-the-art (SOTA) 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 demonstrate the superior performance of our algorithm to existing fault localization algorithms in terms of both accuracy and model interpretability.

Interpretable models are crucial in many high-stakes decision-making applications. In this paper, we focus on learning a decision rule list for binary and multi-class classification. Different from rule set learning problems, learning an optimal rule list involves not only learning a set of rules, but also their orders. In addition, many existing algorithms rely on rule pre-mining to handle large-scale high-dimensional data, which leads to suboptimal rule list model and degrades its generalization accuracy and interpretablity. In this paper, we learn a rule list from the sequence submodular perspective. We consider the rule list as a sequence and define the cover set for each rule. Then we formulate a sequence function which combines both model complexity and classification accuracy. Based on its appealing sequence submodular property, we propose a general distorted greedy insert algorithm under Minorization-Maximization (MM) framework, which gradually inserts rules with highest inserting gain to the rule list. The rule generation process is treated as a subproblem, allowing our method to learn the rule list through a unified framework which avoids rule pre-mining. We further provide a theoretical lower bound of our greedy insert algorithm in rule list learning. Experimental results show that our algorithm achieves better accuracy and interpretability than the state-of-the-art rule learning methods, and in particular it scales well on large-scale datasets, especially on high-dimensional data.

Rule sets are highly interpretable logical models in which the predicates for decision are expressed in disjunctive normal form (DNF, OR-of-ANDs), or, equivalently, the overall model comprises an unordered collection of if-then decision rules. In this paper, we consider a submodular optimization based approach for learning rule sets. The learning problem is framed as a subset selection task in which a subset of all possible rules needs to be selected to form an accurate and interpretable rule set. We employ an objective function that exhibits submodularity and thus is amenable to submodular optimization techniques. To overcome the difficulty arose from dealing with the exponential-sized ground set of rules, the subproblem of searching a rule is casted as another subset selection task that asks for a subset of features. We show it is possible to write the induced objective function for the subproblem as a difference of two submodular (DS) functions to make it approximately solvable by DS optimization algorithms. Overall, the proposed approach is simple, scalable, and likely to be benefited from further research on submodular optimization. Experiments on real datasets demonstrate the effectiveness of our method.