Selecting electrical billing attributes: big data preprocessing improvements
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 a...
- Autores:
-
Viloria, Amelec
García Guiliany, Jesús Enrique
Orellano Llinás, Nataly
Hernandez-P, Hugo
Steffens Sanabria, Ernesto
Pineda, Omar
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7786
- Acceso en línea:
- https://hdl.handle.net/11323/7786
https://doi.org/10.1007/978-981-15-3125-5_44
https://repositorio.cuc.edu.co/
- Palabra clave:
- Electric billing
Concave programming
Data mining
Electric service billing
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Selecting electrical billing attributes: big data preprocessing improvements |
| title |
Selecting electrical billing attributes: big data preprocessing improvements |
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Selecting electrical billing attributes: big data preprocessing improvements Electric billing Concave programming Data mining Electric service billing |
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Selecting electrical billing attributes: big data preprocessing improvements |
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Selecting electrical billing attributes: big data preprocessing improvements |
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Selecting electrical billing attributes: big data preprocessing improvements |
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Selecting electrical billing attributes: big data preprocessing improvements |
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Selecting electrical billing attributes: big data preprocessing improvements |
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Viloria, Amelec García Guiliany, Jesús Enrique Orellano Llinás, Nataly Hernandez-P, Hugo Steffens Sanabria, Ernesto Pineda, Omar |
| dc.contributor.author.spa.fl_str_mv |
Viloria, Amelec García Guiliany, Jesús Enrique Orellano Llinás, Nataly Hernandez-P, Hugo Steffens Sanabria, Ernesto Pineda, Omar |
| dc.subject.spa.fl_str_mv |
Electric billing Concave programming Data mining Electric service billing |
| topic |
Electric billing Concave programming Data mining Electric service billing |
| description |
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. |
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2021-01-28T13:01:00Z |
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2021-01-28T13:01:00Z |
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Corporación Universidad de la Costa |
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eng |
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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 2. 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 3. 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 4. 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 5. Liu R, Feng G, Ding W (2011) Statistical analysis and application of SAS. China Machine Press, China 6. Ozger M, Cetinkaya O, Akan OB (2017) Energy harvesting cognitive radio networking for IoT-enabled smart grid. Mob Netw Appl 23(4):956–966 7. Isasi P, Galván I (2004) Redes de Neuronas Artificiales. Un enfoque Práctico. Pearson, London. ISBN 8420540250 8. Mangasarian O (1997) Arbitrary-norm separating plane. Technical report 97-07, Computer Science Dept., Univ. Wisconsin Madison 9. Bradley P, Fayyad U, Mangasarian O (1999) Mathematical programming for data mining: formulations and challenges. INFORMS J Comput 11:217–238 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) 11. Gangurde HD (2014) Feature selection using clustering approach for big data. Int J Comput Appl Innov Trends Comput Commun Eng (ITCCE):1–3 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 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 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 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 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 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 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) |
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Viloria, AmelecGarcía Guiliany, Jesús EnriqueOrellano Llinás, NatalyHernandez-P, HugoSteffens Sanabria, ErnestoPineda, Omar2021-01-28T13:01:00Z2021-01-28T13:01:00Z2020https://hdl.handle.net/11323/7786https://doi.org/10.1007/978-981-15-3125-5_44Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/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.Viloria, AmelecGarcía Guiliany, Jesús Enrique-will be generated-orcid-0000-0003-3777-3667-600Orellano Llinás, NatalyHernandez-P, HugoSteffens Sanabria, ErnestoPineda, Omar-will be generated-orcid-0000-0002-8239-3906-600application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Lecture Notes in Electrical Engineeringhttps://link.springer.com/chapter/10.1007/978-981-15-3125-5_44Electric billingConcave programmingData miningElectric service billingSelecting electrical billing attributes: big data preprocessing improvementsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. 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 82–902. 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):3293–32993. 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):1–84. 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):2437–24475. Liu R, Feng G, Ding W (2011) Statistical analysis and application of SAS. China Machine Press, China6. Ozger M, Cetinkaya O, Akan OB (2017) Energy harvesting cognitive radio networking for IoT-enabled smart grid. Mob Netw Appl 23(4):956–9667. Isasi P, Galván I (2004) Redes de Neuronas Artificiales. Un enfoque Práctico. Pearson, London. ISBN 84205402508. Mangasarian O (1997) Arbitrary-norm separating plane. Technical report 97-07, Computer Science Dept., Univ. Wisconsin Madison9. Bradley P, Fayyad U, Mangasarian O (1999) Mathematical programming for data mining: formulations and challenges. INFORMS J Comput 11:217–23810. 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)11. Gangurde HD (2014) Feature selection using clustering approach for big data. Int J Comput Appl Innov Trends Comput Commun Eng (ITCCE):1–312. 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 84:24–3613. 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, Cham14. 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, Cham15. 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, Cham16. 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 618–62517. 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 174–18518. Perez R, Vásquez C, Viloria A (2019) An intelligent strategy for faults location in distribution networks with distributed generation. 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