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Rice Predictive Analysis Mechanism Utilizing Grey Wolf Optimizer-Least Squares Support Vector Machines

Zuriani, Mustaffa and M. H., Sulaiman (2015) Rice Predictive Analysis Mechanism Utilizing Grey Wolf Optimizer-Least Squares Support Vector Machines. ARPN Journal of Engineering and Applied Sciences, 10 (23). pp. 17486-17491. ISSN 1819-6608

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: Gold price predictive analysis; grey wolf optimizer; least square support vector machines
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division: Faculty of Computer System And Software Engineering
Faculty of Electrical & Electronic Engineering
Depositing User: Dr. Zuriani Mustaffa
Date Deposited: 22 Feb 2017 05:43
Last Modified: 26 Feb 2018 07:57
URI: http://umpir.ump.edu.my/id/eprint/16363
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