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基于遗传算法和支持向量机的混合模型在果汁分类变量选择中的应用。

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

作者信息

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

机构信息

Information and Communications Technologies Department, Faculty of Computer Science, University of A Coruña, Campus Elviña s/n, 15071, A Coruña, Spain.

Analytical Chemistry Department, Faculty of Sciences, University of A Coruña, Campus da Zapateira s/n, 15008, A Coruña, Spain.

出版信息

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

DOI:10.1155/2013/982438
PMID:24453933
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3874306/
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.

摘要

鉴于神经网络在苹果汁分类问题中的应用背景，本文旨在实现机器学习领域一种新开发的方法：支持向量机（SVM）。因此，提出了一种将遗传算法和支持向量机相结合的混合模型，其方式为，当将支持向量机用作遗传算法（GA）的适应度函数时，可以选择特定分类问题的最具代表性的变量。

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