• Classified by Research Topic • Sorted by Date • Classified by Publication Type •
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 inConjunctive Normal form (CNF) or in Disjunctive Normal form(DNF) are arguably some of the most interpretable ones. AlthoughCNF/DNF rules are considered interpretable in practice, propositionallogic has other very interpretable representations which aremore expressive. In this paper, we generalize CNF/DNF rules andintroduce relaxed-CNF rules, which is motivated by the popular usageof checklists in the medical domain. We consider relaxed definitionsof standard OR/AND operators which allow exceptions inthe construction of a clause and also in the selection of clauses in arule. While the combinatorial structure of relaxed-CNF rules offersexponential succinctness, the naive learning techniques arecomputationally expensive.The primary contribution of this paper is to propose a novelincremental mini-batch learning procedure, called CRR, that employsadvances in the combinatorial solvers and efficiently learnsrelaxed-CNF rules. Our experimental analysis demonstrates that CRR cangenerate relaxed-CNF rules which are more accurate compared toCNF 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.}, }
Generated by bib2html.pl (written by Patrick Riley with layout from Sanjit A. Seshia ) on Thu Aug 22, 2024 18:37:34