LSSVM Parameters Tuning with Enhanced Artificial Bee Colony

To date, exploring an efficient method for optimizing Least Squares Support Vector Machines (LSSVM) hyperparameters has been an enthusiastic research area among academic researchers. LSSVM is a practical machine learning approach that has been broadly utilized in numerous fields. To guarantee its convincing performance, it is crucial to select an appropriate technique in order to obtain the optimized hyper-parameters of LSSVM algorithm. In this paper, an Enhanced Artificial Bee Colony (eABC) is used to obtain the ideal value of LSSVM's hyper parameters, which are regularization parameter, γ and kernel parameter, σ2 . Later, LSSVM is used as the prediction model. The proposed model was employed in predicting financial time series data and comparison is made against the standard Artificial Bee Colony (ABC) and Cross Validation (CV) technique. The simulation results assured the accuracy of parameter selection, thus proved the validity in improving the prediction accuracy with acceptable computational time