Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification
Joint Authors
Fernandez-Lozano, C.
Canto, C.
Gestal, M.
Andrade-Garda, J. M.
Rabuñal, J. R.
Dorado, J.
Pazos, Alejandro
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-12-10
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM).
Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected.
American Psychological Association (APA)
Fernandez-Lozano, C.& Canto, C.& Gestal, M.& Andrade-Garda, J. M.& Rabuñal, J. R.& Dorado, J.…[et al.]. 2013. Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification. The Scientific World Journal،Vol. 2013, no. 2013, pp.1-13.
https://search.emarefa.net/detail/BIM-1033519
Modern Language Association (MLA)
Fernandez-Lozano, C.…[et al.]. Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification. The Scientific World Journal No. 2013 (2013), pp.1-13.
https://search.emarefa.net/detail/BIM-1033519
American Medical Association (AMA)
Fernandez-Lozano, C.& Canto, C.& Gestal, M.& Andrade-Garda, J. M.& Rabuñal, J. R.& Dorado, J.…[et al.]. Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification. The Scientific World Journal. 2013. Vol. 2013, no. 2013, pp.1-13.
https://search.emarefa.net/detail/BIM-1033519
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1033519