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- W1993969106 abstract "Optimizing the hyper-parameters of Least Squares Support Vector Machines (LSSVM) is crucial as it will directly influence the predictive power of the algorithm. To tackle such issue, this study proposes an improved Artificial Bee Colony (IABC) algorithm which is based on conventional mutation. The IABC serves as an optimizer for LSSVM. Realized in gasoline price forecasting, the performance is guided based on Mean Absolute Percentage Error (MAPE) and Root Mean Square Percentage Error (RMSPE). The conducted simulation results show that, the proposed IABC- LSSVM outperforms the results produced by ABC-LSSVM and also the Back Propagation Neural Network." @default.
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- W1993969106 date "2014-05-01" @default.
- W1993969106 modified "2023-09-27" @default.
- W1993969106 title "Gasoline Price Forecasting: An Application of LSSVM with Improved ABC" @default.
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- W1993969106 doi "https://doi.org/10.1016/j.sbspro.2014.03.718" @default.
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