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Selecting electrical billing attributes: big data preprocessing improvements
dc.contributor.author | Viloria, Amelec | spa |
dc.contributor.author | García Guiliany, Jesús Enrique | spa |
dc.contributor.author | Orellano Llinás, Nataly | spa |
dc.contributor.author | Hernandez-P, Hugo | spa |
dc.contributor.author | Steffens Sanabria, Ernesto | spa |
dc.contributor.author | Pineda, Omar | spa |
dc.date.accessioned | 2021-01-28T13:01:00Z | |
dc.date.available | 2021-01-28T13:01:00Z | |
dc.date.issued | 2020 | |
dc.identifier.uri | https://hdl.handle.net/11323/7786 | spa |
dc.description.abstract | The 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.mimetype | application/pdf | spa |
dc.language.iso | eng | |
dc.publisher | Corporación Universidad de la Costa | spa |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | spa |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | spa |
dc.source | Lecture Notes in Electrical Engineering | spa |
dc.subject | Electric billing | spa |
dc.subject | Concave programming | spa |
dc.subject | Data mining | spa |
dc.subject | Electric service billing | spa |
dc.title | Selecting electrical billing attributes: big data preprocessing improvements | spa |
dc.type | Artículo de revista | spa |
dc.source.url | https://link.springer.com/chapter/10.1007/978-981-15-3125-5_44 | spa |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.identifier.doi | https://doi.org/10.1007/978-981-15-3125-5_44 | spa |
dc.identifier.instname | Corporación Universidad de la Costa | spa |
dc.identifier.reponame | REDICUC - Repositorio CUC | spa |
dc.identifier.repourl | https://repositorio.cuc.edu.co/ | spa |
dc.relation.references | 1. 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 82–90 | spa |
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dc.relation.references | 7. Isasi P, Galván I (2004) Redes de Neuronas Artificiales. Un enfoque Práctico. Pearson, London. ISBN 8420540250 | spa |
dc.relation.references | 8. Mangasarian O (1997) Arbitrary-norm separating plane. Technical report 97-07, Computer Science Dept., Univ. Wisconsin Madison | spa |
dc.relation.references | 9. Bradley P, Fayyad U, Mangasarian O (1999) Mathematical programming for data mining: formulations and challenges. INFORMS J Comput 11:217–238 | spa |
dc.relation.references | 10. 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.references | 11. Gangurde HD (2014) Feature selection using clustering approach for big data. Int J Comput Appl Innov Trends Comput Commun Eng (ITCCE):1–3 | spa |
dc.relation.references | 12. Abualigah LM, Khader AT, Al-Beta MA, Alomari OA (2017) Text feature selection with a robust weight scheme and dynamic dimension reduction to text document clustering. Expert Syst Appl 84:24–36 | spa |
dc.relation.references | 13. 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, Cham | spa |
dc.relation.references | 14. 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, Cham | spa |
dc.relation.references | 15. 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, Cham | spa |
dc.relation.references | 16. 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 618–625 | spa |
dc.relation.references | 17. 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 174–185 | spa |
dc.relation.references | 18. 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 |
dc.type.coar | http://purl.org/coar/resource_type/c_6501 | spa |
dc.type.content | Text | spa |
dc.type.driver | info:eu-repo/semantics/article | spa |
dc.type.redcol | http://purl.org/redcol/resource_type/ART | spa |
dc.type.version | info:eu-repo/semantics/acceptedVersion | spa |
dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
dc.rights.coar | http://purl.org/coar/access_right/c_abf2 | spa |
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