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dc.creatorViloria, Amelec
dc.creatorGarcía Guiliany, Jesús Enrique
dc.creatorOrellano Llinás, Nataly
dc.creatorHernandez-P, Hugo
dc.creatorSteffens Sanabria, Ernesto
dc.creatorPineda, Omar
dc.date.accessioned2021-01-28T13:01:00Z
dc.date.available2021-01-28T13:01:00Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/11323/7786
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.isoengspa
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
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.typearticlespa
dcterms.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
dcterms.references2. Hu C, Du S, Su J et al (2016) Discussion on the ways of purchasing and selling electricity and the mode of operation in China’s electricity sales companies under the background of new electric power reform. Power Netw Technol 40(11):3293–3299spa
dcterms.references3. Xue Y, Lai Y (2016) The integration of great energy thinking and big datas thinking: big data and electricity big data. Power Syst Autom 40(1):1–8spa
dcterms.references4. Wang Y, Chen Q, Kang C et al (2017) Clustering of electricity consumption behaviour dynamics toward big data applications. IEEE Trans Smart Grid 7(5):2437–2447spa
dcterms.references5. Liu R, Feng G, Ding W (2011) Statistical analysis and application of SAS. China Machine Press, Chinaspa
dcterms.references6. Ozger M, Cetinkaya O, Akan OB (2017) Energy harvesting cognitive radio networking for IoT-enabled smart grid. Mob Netw Appl 23(4):956–966spa
dcterms.references7. Isasi P, Galván I (2004) Redes de Neuronas Artificiales. Un enfoque Práctico. Pearson, London. ISBN 8420540250spa
dcterms.references8. Mangasarian O (1997) Arbitrary-norm separating plane. Technical report 97-07, Computer Science Dept., Univ. Wisconsin Madisonspa
dcterms.references9. Bradley P, Fayyad U, Mangasarian O (1999) Mathematical programming for data mining: formulations and challenges. INFORMS J Comput 11:217–238spa
dcterms.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
dcterms.references11. Gangurde HD (2014) Feature selection using clustering approach for big data. Int J Comput Appl Innov Trends Comput Commun Eng (ITCCE):1–3spa
dcterms.references12. 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 84:24–36spa
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dcterms.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
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionspa
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_44


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