Determination of support vector regression parameters using African buffalo optimization algorithm

Inusa Sani Maijama’a, Yuhanis Yusof, Mohamad Farhan Mohsin


The use of support vector regression (SVR) for regression tasks has been on increase over the past few years. Unfortunately, the practical application of SVR for regression task is limited due to its dependence on proper setting of its hyper-parameters and associated kernel parameter. Therefore, it become imperative to device a reliable and fast mechanism of determining the value of these parameters that could guarantee lowest generalization error. This paper presents SVR parameter optimization approaches using African buffalo optimisation (ABO) algorithm, i.e. SVR-ABO. The SVR parameters are optimized by using African buffalo optimisation algorithm. Results obtained from several experiments performed has shown that the proposed ABO algorithm has the capability of determining SVR hyper-parameters which most of time has to be done through estimation.


African buffalo optimisation; Parameter optimisation; Support vector regression; Machine learning

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