Machine learning has become omnipresent with applications in various
safety-critical domains such as medical, law, and transportation. In these
domains, high-stake decisions provided by machine learning necessitate
researchers to design interpretable models, where the prediction is
understandable to a human. In interpretable machine learning, rule-based
classifiers are particularly effective in representing the decision boundary
through a set of rules comprising input features. The interpretability of
rule-based classifiers is in general related to the size of the rules, where
smaller rules are considered more interpretable. To learn such a classifier,
the brute-force direct approach is to consider an optimization problem that
tries to learn the smallest classification rule that has close to maximum
accuracy. This optimization problem is computationally intractable due to its
combinatorial nature and thus, the problem is not scalable in large datasets.
To this end, in this paper we study the triangular relationship among the
accuracy, interpretability, and scalability of learning rule-based classifiers.
The contribution of this paper is an interpretable learning framework IMLI,
that is based on maximum satisfiability (MaxSAT) for synthesizing
classification rules expressible in proposition logic. Despite the progress of
MaxSAT solving in the last decade, the straightforward MaxSAT-based solution
cannot scale. Therefore, we incorporate an efficient incremental learning
technique inside the MaxSAT formulation by integrating mini-batch learning and
iterative rule-learning. In our experiments, IMLI achieves the best balance
among prediction accuracy, interpretability, and scalability. As an
application, we deploy IMLI in learning popular interpretable classifiers such
as decision lists and decision sets.

本文介绍了一种名为 IMLI 的基于最大可满足性的可解释学习框架，通过结合小批量学习和迭代规则学习的方法，实现了分类规则的合成。实验结果表明，IMLI 在预测准确性、可解释性和可伸缩性之间取得了最好的平衡，并用于学习流行的可解释分类器，如决策列表和决策集。

可解释分类规则的高效学习

Efficient Learning of Interpretable Classification Rules

We present POTATO, a task- and languageindependent framework for
human-in-the-loop (HITL) learning of rule-based text classifiers using
graph-based features. POTATO handles any type of directed graph and supports
parsing text into Abstract Meaning Representations (AMR), Universal
Dependencies (UD), and 4lang semantic graphs. A streamlit-based user interface
allows users to build rule systems from graph patterns, provides real-time
evaluation based on ground truth data, and suggests rules by ranking graph
features using interpretable machine learning models. Users can also provide
patterns over graphs using regular expressions, and POTATO can recommend
refinements of such rules. POTATO is applied in projects across domains and
languages, including classification tasks on German legal text and English
social media data. All components of our system are written in Python, can be
installed via pip, and are released under an MIT License on GitHub.

本研究提出了 POTATO 框架，支持基于规则的文本分类器的人机协同学习，并使用基于图形的特征处理任何类型的有向图，同时支持解析文本为抽象意义表示，通用依赖和 4lang 语义图并提供基于 Streamlit 的用户界面。