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An Inductive Methodology for Data-Based Rules Building

dc.book.titleAlgorithmic Decision Theory: First International Conference, ADT 2009, Venice, Italy, October 2009, Proceedings
dc.contributor.authorRodríguez González, Juan Tinguaro
dc.contributor.authorMontero De Juan, Francisco Javier
dc.contributor.authorVitoriano Villanueva, Begoña
dc.contributor.authorLópez, Victoria
dc.contributor.editorRossi, Francesca
dc.contributor.editorTsoukias, Alexis
dc.date.accessioned2023-06-20T13:38:26Z
dc.date.available2023-06-20T13:38:26Z
dc.date.issued2009
dc.descriptionLecture Notes in Artificial Intelligence
dc.description.abstractExtraction of rules from databases for classification and decision tasks is an issue of growing importance as automated processes based on data are being required in these fields. An inductive methodology for data-based rules building and automated learning is presented in this paper. A fuzzy framework is used for knowledge representation and, through the introduction and the use of dual properties in the valuation space of response variables, reasons for and against the rules are evaluated from data. This make possible to use continuous DDT logic, which provides a more general and informative framework, in order to assess the validity of rules and build an appropriate knowledge base.en
dc.description.departmentDepto. de Estadística e Investigación Operativa
dc.description.facultyFac. de Ciencias Matemáticas
dc.description.refereedTRUE
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/16858
dc.identifier.citationRodríguez, J.T., Montero, J., Vitoriano, B., López, V.: An Inductive Methodology for Data-Based Rules Building. En: Rossi, F. y Tsoukias, A. (eds.) Algorithmic Decision Theory. pp. 424-433. Springer Berlin Heidelberg, Berlin, Heidelberg (2009)
dc.identifier.doi10.1007/978-3-642-04428-1_37
dc.identifier.isbn978-3-642-04427-4
dc.identifier.officialurlhttps//doi.org/10.1007/978-3-642-04428-1_37
dc.identifier.relatedurlhttp://www.springerlink.com/content/b650283049324378/fulltext.pdf
dc.identifier.urihttps://hdl.handle.net/20.500.14352/53154
dc.issue.number5783
dc.language.isoeng
dc.page.final433
dc.page.initial424
dc.publication.placeBerlin
dc.publisherSpringer-Verlag Berlin Heidelberg
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.relation.projectIDTIN200606190
dc.relation.projectIDIC0602
dc.relation.projectIDCCGO7-UCM/ESP-2576
dc.relation.projectIDC3-0132
dc.rights.accessRightsrestricted access
dc.subject.cdu519.83
dc.subject.keywordRules induction
dc.subject.keywordDDT logic
dc.subject.keywordFuzzy inference systems
dc.subject.keywordDual predicates
dc.subject.ucmInvestigación operativa (Matemáticas)
dc.subject.unesco1207 Investigación Operativa
dc.titleAn Inductive Methodology for Data-Based Rules Buildingen
dc.typebook part
dcterms.referencesAmo, A., Montero, J., Biging, G., Cutello, V.: Fuzzy classification systems. European Journal of Operational Research 156(2), 495–507 (2004) Atanassov, K.T.: Intuitionistic Fuzzy Sets. Physica-Verlag, Heidelberg (1999) Destercke, S., Guillaume, S., Charnomordic, B.: Building an interpretable fuzzy rule base from data using Orthogonal Least Squares. Application to a depollution problem. Fuzzy Sets and Systems 158(18), 2078–2094 (2007) Fortemps, P., Slowinski, R.: A graded quadrivalent logic for ordinal preference modelling:Loyola-like approach. Fuzzy Optimization and Decision Making 1(1), 93–111 (2002) Fortemps, P., Greco, S., Slowinski, R.: Multicriteria decision support using rules that represent rough-graded preference relations. European J. Operational Research 188(1),206–223 (2008) Hammer, P.L., Bonates, T.: Logical Analysis of Data - An overview: From Combinatorial Optimization to Medical Applications. Annals of Operations Research 148(1),203–225(2006) Iliadis, L.S.: A decision support system applying an integrated fuzzy model for long-term forest fire risk estimation. Environmental Modelling & Software 20, 613–621 (2005) Mamdani, E.H.: Application of Fuzzy Algorithms for the Control of a Dynamic Plant.Proc. IEE 121(12), 1585–1588 (1974) Montero, J., Gómez, D., Bustince, H.: On the relevance of some families of fuzzy sets.Fuzzy sets and systems 158(22), 2439–2442 (2007) Novak, V.: Antonyms and linguistic quantifiers in fuzzy logic. Fuzzy Sets and Systems 124, 335–351 (2001) Öztürk, M., Tsoukiàs, A.: Modelling uncertain positive and negative reasons in decision aiding. Decision Support Systems 43(4), 1512–1526 (2007) Paradis, C., Willners, C.: Antonymy and negation—the boundness hypothesis. J. Pragmatics 38, 1051–1080 (2006) Rodriguez, J.T., Vitoriano, B., Montero, J., Omaña, A.: A decision support tool for humanitarian organizations in natural disaster relief. In: Ruan, D., et al. (eds.) Computational Intelligence in Decision and Control, pp. 600–605. World Scientific, Singapore (2008) Ruspini, E.H.: A new approach to clustering. Inform.Control 15, 22–32 (1969) Tsoukiàs, A.: A first-order, four valued, weakly paraconsistent logic and its relation to rough sets semantics. Foundations of Computing and Decision Sciences 12, 85–108 (2002) Yager, R.R.: Targeted e-commerce marketing using fuzzy intelligent agents. Intelligent Systems and their Applications 15(6), 42–45 (2000)
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