Development of fast algorithms for reduct computation

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dc.audiencegeneralPublices_MX
dc.contributorOlvera López, José Arturo
dc.contributorMartínez Trinidad, José Francisco
dc.contributorLazo Cortés, Manuel Sabino
dc.contributor.advisorOlvera López, José Arturo; 48066
dc.contributor.advisorMartínez Trinidad, José Francisco; 20250
dc.contributor.advisorLAZO CORTES, MANUEL SABINO; 493154
dc.contributor.authorRodríguez Diez, Vladímir
dc.creatorRODRIGUEZ DIEZ, VLADIMIR; 634370
dc.date.accessioned2023-05-09T22:06:57Z
dc.date.available2023-05-09T22:06:57Z
dc.date.issued2022-12
dc.description.abstract"Rough Set Theory deals with imperfect knowledge in machine learning. Within Rough Set Theory, reducts are minimal subsets of attributes that preserve the discernibility power of the complete set of attributes in a dataset. Reducts are specially useful as an attribute reduction technique for classification and data storage. Unfortunately, computing all reducts of a dataset has exponential complexity regarding the number of attributes. Therefore, we proposed here a hardware approach for computing all reducts. The proposed platform outperforms previous hardware and software implementations in terms of runtime. In addition, a new algorithm for computing all reducts that uses simple operations for candidate evaluation, which is the fastest algorithm for an specific kind of datasets, was introduced. Furthermore, an experimental study for finding a relation between some properties of a dataset and the fastest algorithms for computing reducts, is presented. This study provides a guide for determining the appropriated algorithm for a specific problem. We proposed finally a new algorithm for computing all the shortest reducts. This new algorithm outperforms all other state–of–the–art algorithms for most datasets".es_MX
dc.folio20221122124657-8002-Tes_MX
dc.formatpdfes_MX
dc.identificator7es_MX
dc.identifier.urihttps://hdl.handle.net/20.500.12371/18377
dc.language.isoenges_MX
dc.matricula.creator219570152es_MX
dc.publisherBenemérita Universidad Autónoma de Pueblaes_MX
dc.rights.accesopenAccesses_MX
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0es_MX
dc.subject.classificationINGENIERÍA Y TECNOLOGÍAes_MX
dc.subject.lccTeoría de conjuntos--Investigaciónes_MX
dc.subject.lccConjuntos difusoses_MX
dc.subject.lccProcesamiento electrónico de datoses_MX
dc.subject.lccAlgoritmos computacionaleses_MX
dc.thesis.careerDoctorado en Ingeniería del Lenguaje y del Conocimientoes_MX
dc.thesis.degreedisciplineÁrea de Ingeniería y Ciencias Exactases_MX
dc.thesis.degreegrantorFacultad de Ciencias de la Computaciónes_MX
dc.thesis.degreetoobtainDoctor en Ingeniería del Lenguaje y del Conocimientoes_MX
dc.titleDevelopment of fast algorithms for reduct computationes_MX
dc.typeTesis de doctoradoes_MX
dc.type.conacytdoctoralThesises_MX
dc.type.degreeDoctoradoes_MX
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