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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Alves, S.S.A., Dias, M.L.D., da Rocha Neto, A.R., Freire, A.L. (2017). Evolutionary Support Vector Regression via Genetic Algorithms: A Dual Approach. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10305. Springer, Cham. https://doi.org/10.1007/978-3-319-59153-7_8
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