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Merging local patterns using an evolutionary approach

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dc.contributor.author Gaya López, María Cruz spa
dc.contributor.author Giráldez Betrón, José Ignacio spa
dc.date.accessioned 2013-11-27T17:26:29Z
dc.date.available 2013-11-27T17:26:29Z
dc.date.issued 2011 spa
dc.identifier.citation Gaya-López, M. C., & Giráldez-Betrón, J. I. (2011). Merging local patterns using an evolutionary approach. Knowledge and information systems, 29(1), 1-24. spa
dc.identifier.issn 02191377 spa
dc.identifier.uri http://hdl.handle.net/11268/766
dc.description.abstract This paper describes a Decentralized Agent-based model for Theory Synthesis (DATS) implemented by MASETS, a Multi-Agent System for Evolutionary Theory Synthesis. The main contributions are the following: first, a method for the synthesis of a global theory from distributed local theories. Second, a conflict resolution mechanism, based on genetic algorithms, that deals with collision/contradictions in the knowledge discovered by different agents at their corresponding locations. Third, a system-level classification procedure that improves the results obtained from both: the monolithic classifier and the best local classifier. And fourth, a method for mining very large datasets that allows for divide-and-conquer mining followed by merging of discoveries. The model is validated with an experimental application run on 15 datasets. Results show that the global theory outperforms all the local theories, and the monolithic theory (obtained from mining the concatenation of all the available distributed data), in a statistically significant way. spa
dc.language.iso eng spa
dc.subject.other Multi-Database Mining spa
dc.subject.other Genetic Algorithms spa
dc.subject.other Distributed Data Mining spa
dc.subject.other Multi-Agent Systems spa
dc.subject.other High-Frequency Rules spa
dc.subject.other Stacked Generalization spa
dc.subject.other Ensemble Construction spa
dc.subject.other Combining Classifiers spa
dc.subject.other Classification spa
dc.subject.other Algorithms spa
dc.subject.other Computer Science spa
dc.title Merging local patterns using an evolutionary approach spa
dc.type article spa
dc.description.impact 2.225 JCR (2011) Q1, 21/111 Computer science, artificial intelligence, 18/135 Computer science, information systems spa
dc.identifier.doi 10.1007/s10115-010-0332-x spa
dc.rights.accessRights closedAccess en
dc.subject.unesco Recuperación de información spa
dc.subject.unesco Inteligencia artificial spa
dc.description.filiation UEM spa
dc.relation.publisherversion http://ezproxy.universidadeuropea.es/login?url=http:/ /dx.doi.org/10.1007/s10115-010-0332-x spa
dc.peerreviewed Si spa


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