Hybrid model based on Genetic Algorithms and SVM applied to variable selection within fruit juice classification

C Fernandez-Lozano; C Canto; M Gestal; J M Andrade-Garda; J R Rabuñal; J Dorado; A Pazos

doi:10.1155/2013/982438

Hybrid model based on Genetic Algorithms and SVM applied to variable selection within fruit juice classification

ScientificWorldJournal. 2013 Dec 10:2013:982438. doi: 10.1155/2013/982438. eCollection 2013.

Authors

C Fernandez-Lozano¹, C Canto¹, M Gestal¹, J M Andrade-Garda², J R Rabuñal¹, J Dorado¹, A Pazos¹

Affiliations

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

Abstract

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Beverages* / analysis
Beverages* / classification
Fruit / chemistry*
Malus*
Neural Networks, Computer*
Support Vector Machine*