Rice Predictive Analysis Mechanism Utilizing Grey Wolf Optimizer-Least Squares Support Vector Machines - Zuriani, Mustaffa; M. H., Sulaiman (Asian Research Publishing Network (ARPN), 2015)
A good selection of Least Squares Support Vector Machines (LSSVM) hyper-parameters' value is crucial in order to obtain a promising generalization on the unseen data. Any inappropriate value set to the hyper parameters would directly demote the prediction performance of LSSVM. In this regard, this study proposes a hybridization of LSSVM with a new Swarm Intelligence (SI) algorithm namely, Grey Wolf Optimizer (GWO). With such hybridization, the hyper-parameters of interest are automatically optimized by the GWO. The performance of GWO-LSSVM is realized in predictive analysis of gold price and measured based on two indices viz. Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSPE). Findings of the study suggested that the GWO-LSSVM possess lower prediction error rate as compared to three comparable algorithms which includes hybridization models of LSSVM and Evolutionary Computation (EC) algorithms.