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@COMMENT This file came from Kuldeep S. Meel's publication pages at
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@article{GMM22,
  author={Ghosh, Bishwamittra and Malioutov, Dmitry and Meel, Kuldeep S.},
  title={Efficient Learning of Interpretable Classification Rules},
  journal=JAIR,
  volume={74},
  pages={1823--1863},
  year={2022},
  month=sep,
  bib2html_rescat={Formal Methods 4 ML},
  url={https://doi.org/10.1613/jair.1.13482},
  bib2html_pubtype={Journal},
  abstract={
    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. Examples of such
    classifiers include decision trees, decision lists, and decision sets. 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. IMLI considers a
    joint objective function to optimize the accuracy and the interpretability
    of classification rules and learns an optimal rule by solving an
    appropriately designed MaxSAT query. Despite the progress of MaxSAT solving
    in the last decade, the straightforward MaxSAT-based solution cannot scale
    to practical classification datasets containing thousands to millions of
    samples. Therefore, we incorporate an efficient incremental learning
    technique inside the MaxSAT formulation by integrating mini-batch learning
    and iterative rule-learning. The resulting framework learns a classifier by
    iteratively covering the training data, wherein in each iteration, it solves
    a sequence of smaller MaxSAT queries corresponding to each mini-batch. In
    our experiments, IMLI achieves the best balance among prediction accuracy,
    interpretability, and scalability. For instance, IMLI attains a competitive
    prediction accuracy and interpretability w.r.t. existing interpretable
    classifiers and demonstrates impressive scalability on large datasets where
    both interpretable and non-interpretable classifiers fail. As an
    application, we deploy IMLI in learning popular interpretable classifiers
    such as decision lists and decision sets. The source code is available at
    https://github.com/meelgroup/mlic.
  },
}