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Classification Rules in Relaxed Logical Form.
Bishwamittra Ghosh, Dmitry Malioutov and Kuldeep S. Meel.
In Proceedings of European Conference on Artificial Intelligence(ECAI), June 2020.
ML algorithms that produce rule-based predictions in Conjunctive Normal form (CNF) or in Disjunctive Normal form (DNF) are arguably some of the most interpretable ones. Although CNF/DNF rules are considered interpretable in practice, propositional logic has other very interpretable representations which are more expressive. In this paper, we generalize CNF/DNF rules and introduce relaxed-CNF rules, which is motivated by the popular usage of checklists in the medical domain. We consider relaxed definitions of standard OR/AND operators which allow exceptions in the construction of a clause and also in the selection of clauses in a rule. While the combinatorial structure of relaxed-CNF rules offers exponential succinctness, the naive learning techniques are computationally expensive. The primary contribution of this paper is to propose a novel incremental mini-batch learning procedure, called CRR, that employs advances in the combinatorial solvers and efficiently learns relaxed-CNF rules. Our experimental analysis demonstrates that CRR can generate relaxed-CNF rules which are more accurate compared to CNF rules and sparser compared to decision lists.
@inproceedings{GMM20,
title={Classification Rules in Relaxed Logical Form},
author={Ghosh, Bishwamittra and Malioutov, Dmitry and Meel, Kuldeep S.},
booktitle=ECAI,
month=jun,
year={2020},
bib2html_dl_pdf={../Papers/ecai20.pdf},
code={https://github.com/meelgroup/mlic},
bib2html_pubtype={Refereed Conference},
bib2html_rescat={Formal Methods 4 ML},
abstract={
ML algorithms that produce rule-based predictions in
Conjunctive Normal form (CNF) or in Disjunctive Normal form
(DNF) are arguably some of the most interpretable ones. Although
CNF/DNF rules are considered interpretable in practice, propositional
logic has other very interpretable representations which are
more expressive. In this paper, we generalize CNF/DNF rules and
introduce relaxed-CNF rules, which is motivated by the popular usage
of checklists in the medical domain. We consider relaxed definitions
of standard OR/AND operators which allow exceptions in
the construction of a clause and also in the selection of clauses in a
rule. While the combinatorial structure of relaxed-CNF rules offers
exponential succinctness, the naive learning techniques are
computationally expensive.
The primary contribution of this paper is to propose a novel
incremental mini-batch learning procedure, called CRR, that employs
advances in the combinatorial solvers and efficiently learns
relaxed-CNF rules. Our experimental analysis demonstrates that CRR can
generate relaxed-CNF rules which are more accurate compared to
CNF rules and sparser compared to decision lists.
},
}
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