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dc.contributor.authorViloria, Amelecspa
dc.contributor.authorGarcía Guiliany, Jesús Enriquespa
dc.contributor.authorOrellano Llinás, Natalyspa
dc.contributor.authorHernandez-P, Hugospa
dc.contributor.authorSteffens Sanabria, Ernestospa
dc.contributor.authorPineda, Omarspa
dc.date.accessioned2021-01-28T13:01:00Z
dc.date.available2021-01-28T13:01:00Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/11323/7786spa
dc.description.abstractThe attribute selection is a very relevant activity of data preprocessing when discovering knowledge on databases. Its main objective is to eliminate irrelevant and/or redundant attributes to obtain computationally treatable issues, without affecting the quality of the solution. Various techniques are proposed, mainly from two approaches: wrapper and ranking. This article evaluates a novel approach proposed by Bradley and Mangasarian (Machine learning ICML. Morgan Kaufmann, Sn Fco, CA, pp. 82–90, 1998 [1]) which uses concave programming for minimizing the classification error and the number of attributes required to perform the task. The technique is evaluated using the electric service billing database in Colombia. The results are compared against traditional techniques for evaluating: attribute reduction, processing time, discovered knowledge size, and solution quality.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoeng
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.sourceLecture Notes in Electrical Engineeringspa
dc.subjectElectric billingspa
dc.subjectConcave programmingspa
dc.subjectData miningspa
dc.subjectElectric service billingspa
dc.titleSelecting electrical billing attributes: big data preprocessing improvementsspa
dc.typeArtículo de revistaspa
dc.source.urlhttps://link.springer.com/chapter/10.1007/978-981-15-3125-5_44spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttps://doi.org/10.1007/978-981-15-3125-5_44spa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
dc.relation.references1. Bradley P, Mangasarian O (1998) Feature selection via concave minimization and support vector machines. In: Shavlik J (ed) Machine learning ICML. Morgan Kaufmann, Sn Fco, CA, pp 82–90spa
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dc.relation.references7. Isasi P, Galván I (2004) Redes de Neuronas Artificiales. Un enfoque Práctico. Pearson, London. ISBN 8420540250spa
dc.relation.references8. Mangasarian O (1997) Arbitrary-norm separating plane. Technical report 97-07, Computer Science Dept., Univ. Wisconsin Madisonspa
dc.relation.references9. Bradley P, Fayyad U, Mangasarian O (1999) Mathematical programming for data mining: formulations and challenges. INFORMS J Comput 11:217–238spa
dc.relation.references10. Rahmani AM, Liljeberg P, Preden J, Jantsch A (2018) Fog computing in the internet of things. Springer, New York ISBN: 978-3-319-57638-1, ISBN: 978-3-319-57639-8 (eBook)spa
dc.relation.references11. Gangurde HD (2014) Feature selection using clustering approach for big data. Int J Comput Appl Innov Trends Comput Commun Eng (ITCCE):1–3spa
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dc.relation.references13. Sanchez L, Vásquez C, Viloria A, Cmeza-estrada (2018) Conglomerates of Latin American countries and public policies for the sustainable development of the electric power generation sector. In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture notes in computer science, vol 10943. Springer, Chamspa
dc.relation.references14. Sánchez L, Vásquez C, Viloria A, Rodríguez Potes L (2018) Greenhouse gases emissions and electric power generation in Latin American countries in the period 2006–2013. In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture notes in computer science, vol 10943. Springer, Chamspa
dc.relation.references15. Perez R et al (2018) Fault diagnosis on electrical distribution systems based on fuzzy logic. In: Tan Y, Shi Y, Tang Q (eds) Advances in swarm intelligence. ICSI 2018. Lecture notes in computer science, vol 10942. Springer, Chamspa
dc.relation.references16. Silva V, Jesús A (2013) Indicators systems for evaluating the efficiency of political awareness of rational use of electricity. In: Advanced materials research, vol 601. Trans Tech Publications, Switzerland, pp 618–625spa
dc.relation.references17. Perez R, Inga E, Aguila A, Vásquez C, Lima L, Viloria A, Henry MA (2018) Fault diagnosis on electrical distribution systems based on fuzzy logic. In: International conference on sensing and imaging, June. Springer, Cham, pp 174–185spa
dc.relation.references18. Perez R, Vásquez C, Viloria A (2019) An intelligent strategy for faults location in distribution networks with distributed generation. J Intell Fuzzy Syst 36(2):1627–1637 (Preprint)spa
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dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa


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