Johannes Fürnkranz, Van Quoc Huynh, Florian Beck,
"On the Incremental Construction of Deep Rule Theories"
, in Lucie Ciencialova, Martin Holena, Robert Jajcay, Tatiana Jajcayova, Frantisek Mraz, Dana Pardubska, Martin Platek: Proceedings of the 22nd Conference Information Technologies - Applications and Theory (ITAT), Serie CEUR Workshop Proceedings, Vol. 3226, CEUR-WS.org, Zuberec, Slovakia, Seite(n) 21-27, 2022
Original Titel:
On the Incremental Construction of Deep Rule Theories
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 22nd Conference Information Technologies - Applications and Theory (ITAT)
Original Kurzfassung:
While we have seen considerable progress in learning rule-based theories in the past years, all state-of-the-art rule learners
still learn descriptions that directly relate the input features to the target concept. However, the limitation of learning
concepts in this shallow disjunctive normal form (DNF) may not necessarily lead to the desired models. In this paper, we
investigate a novel search strategy that uses conjunctions and disjunctions of individuals as its elementary operators, which
are successively combined to deep rule structures with intermediate concepts. We make use of efficient data structures known
from association rule mining, which can efficiently summarize counts of conjunctive expressions, and expand them to handle
disjunctive expressions as well. The resulting rule concepts develop over multiple generations and consist of arbitrary, deep
combinations of conjunctions and disjunctions. The behavior of this algorithm is evaluated on a benchmark task from the
domain of poker. A comparison to other rule learning algorithms shows that, while it is not generally competitive, it has some
interesting properties, such as finding more compact and better generalizing models, that cannot be found in conventional
rule learning algorithms.
Forschungs-