Selecting electrical billing attributes: big data preprocessing improvements

dc.creatorViloria, Amelec
dc.creatorGarcía Guiliany, Jesús Enrique
dc.creatorOrellano Llinás, Nataly
dc.creatorHernandez-P, Hugo
dc.creatorSteffens Sanabria, Ernesto
dc.creatorPineda, Omar
dc.date2021-01-28T13:01:00Z
dc.date2021-01-28T13:01:00Z
dc.date2020
dc.date.accessioned2023-10-03T20:03:46Z
dc.date.available2023-10-03T20:03:46Z
dc.identifierhttps://hdl.handle.net/11323/7786
dc.identifierhttps://doi.org/10.1007/978-981-15-3125-5_44
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9174142
dc.descriptionThe 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.
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherCorporación Universidad de la Costa
dc.relation1. 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
dc.relation2. 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):3293–3299
dc.relation3. 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):1–8
dc.relation4. 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):2437–2447
dc.relation5. Liu R, Feng G, Ding W (2011) Statistical analysis and application of SAS. China Machine Press, China
dc.relation6. Ozger M, Cetinkaya O, Akan OB (2017) Energy harvesting cognitive radio networking for IoT-enabled smart grid. Mob Netw Appl 23(4):956–966
dc.relation7. Isasi P, Galván I (2004) Redes de Neuronas Artificiales. Un enfoque Práctico. Pearson, London. ISBN 8420540250
dc.relation8. Mangasarian O (1997) Arbitrary-norm separating plane. Technical report 97-07, Computer Science Dept., Univ. Wisconsin Madison
dc.relation9. Bradley P, Fayyad U, Mangasarian O (1999) Mathematical programming for data mining: formulations and challenges. INFORMS J Comput 11:217–238
dc.relation10. 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)
dc.relation11. Gangurde HD (2014) Feature selection using clustering approach for big data. Int J Comput Appl Innov Trends Comput Commun Eng (ITCCE):1–3
dc.relation12. 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
dc.relation13. 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
dc.relation14. 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
dc.relation15. 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
dc.relation16. 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
dc.relation17. 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
dc.relation18. 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)
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.sourceLecture Notes in Electrical Engineering
dc.sourcehttps://link.springer.com/chapter/10.1007/978-981-15-3125-5_44
dc.subjectElectric billing
dc.subjectConcave programming
dc.subjectData mining
dc.subjectElectric service billing
dc.titleSelecting electrical billing attributes: big data preprocessing improvements
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_6501
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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