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2017 | OriginalPaper | Buchkapitel

Evolutionary Support Vector Regression via Genetic Algorithms: A Dual Approach

verfasst von : Shara S. A. Alves, Madson L. D. Dias, Ajalmar R. da Rocha Neto, Ananda L. Freire

Erschienen in: Advances in Computational Intelligence

Verlag: Springer International Publishing

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Abstract

Evolutionary machine learning is an emerging research area that covers any combination of evolutionary strategies and machine learning. In support vector machines, metaheuristics have been widely employed to tune parameters, select features or obtain a reduced sub-set of support vectors. However, there are only a few works that aim at embedding evolutionary strategies into the support vector regressors training process, i.e., to apply evolutionary methods to solve the quadratic optimization problem. In this paper, we intend to solve the quadratic optimization problem for support vector regression in its dual formulation by employing genetic algorithms. Our proposal was validated in real-world datasets against state-of-the-art methods, such as sequential minimal optimization, iterative single data algorithm, and a classical mathematical method. The results revealed that our proposal is a competitive alternative, which often reduced the generalization error and achieved sparse solutions.

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Titel: Evolutionary Support Vector Regression via Genetic Algorithms: A Dual Approach
verfasst von: Shara S. A. Alves
Madson L. D. Dias
Ajalmar R. da Rocha Neto
Ananda L. Freire
Verlag: Springer International Publishing
Buch: Advances in Computational Intelligence
Print ISBN: 978-3-319-59152-0

Electronic ISBN: 978-3-319-59153-7

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-59153-7_8

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