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Concise and interpretable multi-label rule sets
Multi-label classification is becoming increasingly ubiquitous, but not much
attention has been paid to interpretability. In this paper, we develop a
multi-label classifier that can be represented as a concise set of simple
"if-then" rules, and thus, it offers better interpretability compared to
black-box models. Notably, our method is able to find a small set of relevant
patterns that lead to accurate multi-label classification, while existing
rule-based classifiers are myopic and wasteful in searching rules,requiring a
large number of rules to achieve high accuracy. In particular, we formulate the
problem of choosing multi-label rules to maximize a target function, which
considers not only discrimination ability with respect to labels, but also
diversity. Accounting for diversity helps to avoid redundancy, and thus, to
control the number of rules in the solution set. To tackle the said
maximization problem we propose a 2-approximation algorithm, which relies on a
novel technique to sample high-quality rules. In addition to our theoretical
analysis, we provide a thorough experimental evaluation, which indicates that
our approach offers a trade-off between predictive performance and
interpretability that is unmatched in previous work
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