Show simple item record

dc.contributor.authorAtserias, Albert
dc.contributor.authorBalcázar Navarro, José Luis
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.identifier.citationAtserias, A., Balcázar, J. L. Entailment among probabilistic implications. A: Annual ACM/IEEE Symposium on Logic in Computer Science. "2015 30th Annual ACM/IEEE Symposium on Logic in Computer Science: LICS 2015: 6–10 July 2015, Kyoto, Japan: proceedings". Kyoto: Institute of Electrical and Electronics Engineers (IEEE), 2015, p. 621-632.
dc.description.abstractWe study a natural variant of the implicational fragment of propositional logic. Its formulas are pairs of conjunctions of positive literals, related together by an implicational-like connective, the semantics of this sort of implication is defined in terms of a threshold on a conditional probability of the consequent, given the antecedent: we are dealing with what the data analysis community calls confidence of partial implications or association rules. Existing studies of redundancy among these partial implications have characterized so far only entailment from one premise and entailment from two premises. By exploiting a previously noted alternative view of this entailment in terms of linear programming duality, we characterize exactly the cases of entailment from arbitrary numbers of premises. As a result, we obtain decision algorithms of better complexity, additionally, for each potential case of entailment, we identify a critical confidence threshold and show that it is, actually, intrinsic to each set of premises and antecedent of the conclusion.
dc.format.extent12 p.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Informàtica::Informàtica teòrica
dc.subject.lcshLinear programming
dc.subject.lcshData mining
dc.subject.otherPartial implication
dc.subject.otherConditional probability
dc.subject.otherConfidence threshold
dc.titleEntailment among probabilistic implications
dc.typeConference report
dc.subject.lemacProgramació lineal
dc.subject.lemacMineria de dades
dc.contributor.groupUniversitat Politècnica de Catalunya. ALBCOM - Algorismia, Bioinformàtica, Complexitat i Mètodes Formals
dc.contributor.groupUniversitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge
dc.description.peerreviewedPeer Reviewed
dc.rights.accessOpen Access
dc.description.versionPostprint (author's final draft)
upcommons.citation.authorAtserias, A., Balcázar, J. L.
upcommons.citation.contributorAnnual ACM/IEEE Symposium on Logic in Computer Science
upcommons.citation.publicationName2015 30th Annual ACM/IEEE Symposium on Logic in Computer Science: LICS 2015: 6–10 July 2015, Kyoto, Japan: proceedings

Files in this item


This item appears in the following Collection(s)

Show simple item record

All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder