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The Scientific World Journal
Volume 2013 (2013), Article ID 982438, 13 pages
Research Article

Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification

1Information and Communications Technologies Department, Faculty of Computer Science, University of A Coruña, Campus Elviña s/n, 15071, A Coruña, Spain
2Analytical Chemistry Department, Faculty of Sciences, University of A Coruña, Campus da Zapateira s/n, 15008, A Coruña, Spain

Received 24 September 2013; Accepted 21 October 2013

Academic Editors: Z. Cui and X. Yang

Copyright © 2013 C. Fernandez-Lozano et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [10 citations]

The following is the list of published articles that have cited the current article.

  • Hongxia Cai, Tingting Yu, and Chenglong Xia, “Quality-Oriented Classification of Aircraft Material Based on SVM,” Mathematical Problems in Engineering, vol. 2014, pp. 1–12, 2014. View at Publisher · View at Google Scholar
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  • Cristian R. Munteanu, Carlos Fernandez-Lozano, Virginia Mato Abad, Salvador Pita Fernández, Juan Álvarez-Linera, Juan Antonio Hernández-Tamames, and Alejandro Pazos, “Classification of mild cognitive impairment and Alzheimer’s disease with machine-learning techniques using 1H Magnetic Resonance Spectroscopy data,” Expert Systems with Applications, 2015. View at Publisher · View at Google Scholar
  • Carlos Fernandez-Lozano, Jose A. Seoane, Marcos Gestal, Tom R. Gaunt, Julian Dorado, and Colin Campbell, “Texture classification using feature selection and kernel-based techniques,” Soft Computing, 2015. View at Publisher · View at Google Scholar
  • Carlos Fernandez-Lozano, Francisco Cedrón, Daniel Rivero, Julian Dorado, José Manuel Andrade-Garda, Alejandro Pazos, and Marcos Gestal, “Using genetic algorithms to improve support vector regression in the analysis of atomic spectra of lubricant oils,” Engineering Computations, vol. 33, no. 4, pp. 995–1005, 2016. View at Publisher · View at Google Scholar
  • Iván Ramírez Morales, Daniel Rivero Cebrián, Enrique Fernández Blanco, and Alejandro Pazos Sierra, “Early warning in egg production curves from commercial hens: A SVM approach,” Computers and Electronics in Agriculture, vol. 121, pp. 169–179, 2016. View at Publisher · View at Google Scholar
  • Yijiang Zhang, “X-ray image enhancement using the fruit fly optimization algorithm,” International Journal of Simulation: Systems, Science and Technology, vol. 17, no. 36, pp. 44.1–44.6, 2016. View at Publisher · View at Google Scholar
  • Chanika Sukawattanavijit, Jie Chen, and Hongsheng Zhang, “GA-SVM Algorithm for Improving Land-Cover Classification Using SAR and Optical Remote Sensing Data,” IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 3, pp. 284–288, 2017. View at Publisher · View at Google Scholar
  • Elham Ahmadi, Milad Jasemi, Leslie Monplaisir, Mohammad Amin Nabavi, Armin Mahmoodi, and Pegah Amini Jam, “New Efficient Hybrid Candlestick Technical Analysis Model for Stock Market Timing on the Basis of the Support Vector Machine and Heuristic Algorithms of Imperialist Competition and Genetic,” Expert Systems with Applications, 2017. View at Publisher · View at Google Scholar
  • Iman Khosravi, Abdolreza Safari, and Saeid Homayouni, “MSMD: maximum separability and minimum dependency feature selection for cropland classification from optical and radar data,” International Journal of Remote Sensing, pp. 1–18, 2018. View at Publisher · View at Google Scholar