Florian Beck, Johannes Fürnkranz, Van Quoc Huynh,
"Generalizing Conjunctive and Disjunctive Rule Learning to Learning m-of-n Concepts"
, in Brejov\'a, Ciencialov\'a, Holena, Jajcay, Jajcayov\'a, Lexa, Mr\'az, Pardubsk\'a, Pl\'atek: Proceedings of the 23rd Conference Information Technologies - Applications and Theory (ITAT 2023), Tatransk\'e Matliare, Slovakia, September 22-26, 2023, Serie CEUR Workshop Proceedings, Vol. 3498, CEUR-WS.org, Seite(n) 8-13, 2023
Original Titel:
Generalizing Conjunctive and Disjunctive Rule Learning to Learning m-of-n Concepts
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 23rd Conference Information Technologies - Applications and Theory (ITAT 2023), Tatransk\'e Matliare, Slovakia, September 22-26, 2023
Original Kurzfassung:
Most rule learning algorithms learn rule concepts as conjunctions and disjunct them afterwards to rule sets, a few others
swap the order of conjunction and disjunction so that rule concepts are learned as disjunctions. Depending on the domain,
both approaches can have advantages or disadvantages in comparison to its counterpart.
Instead of learning rule concepts only as conjunctions or only as disjunctions, one can also flexibly choose between these
two representations. One way to do so is by using m-of-n concepts where m of conditions must be true in order for the
expression to be true. This not only covers the two extreme cases where all conditions must be true (n-of-n, conjunctions) or
any of them must be true (1-of-n, disjunctions) but also a smooth transition for other values of m, analogous to a customizable
activation threshold in neural networks.
In this paper, we discuss possibilities how to efficiently learn m-of-n rules using similar generalization and specialization
operations as for conjunctions or disjunctions. Furthermore, we adjust the state-of-the-art rule learning algorithm LORD to
learn m-of-n concepts instead of plain conjunctions and present an evaluation of the technique on artificial and real-world
data sets.