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

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

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,...

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Main Authors: Zuriani, Mustaffa, M. H., Sulaiman
Format: Article
Language:English
Published: Asian Research Publishing Network (ARPN) 2015
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/16363/
http://umpir.ump.edu.my/id/eprint/16363/
http://umpir.ump.edu.my/id/eprint/16363/1/PRICE%20PREDICTIVE%20ANALYSIS%20MECHANISM%20UTILIZING%20GREY%20WOLF_ARPN.pdf
id ump-16363
recordtype eprints
spelling ump-163632018-02-26T07:57:35Z http://umpir.ump.edu.my/id/eprint/16363/ Rice Predictive Analysis Mechanism Utilizing Grey Wolf Optimizer-Least Squares Support Vector Machines Zuriani, Mustaffa M. H., Sulaiman QA75 Electronic computers. Computer science 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. Asian Research Publishing Network (ARPN) 2015 Article NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/16363/1/PRICE%20PREDICTIVE%20ANALYSIS%20MECHANISM%20UTILIZING%20GREY%20WOLF_ARPN.pdf 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 http://www.arpnjournals.org/jeas/research_papers/rp_2015/jeas_1215_3198.pdf
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Zuriani, Mustaffa
M. H., Sulaiman
Rice Predictive Analysis Mechanism Utilizing Grey Wolf Optimizer-Least Squares Support Vector Machines
description 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.
format Article
author Zuriani, Mustaffa
M. H., Sulaiman
author_facet Zuriani, Mustaffa
M. H., Sulaiman
author_sort Zuriani, Mustaffa
title Rice Predictive Analysis Mechanism Utilizing Grey Wolf Optimizer-Least Squares Support Vector Machines
title_short Rice Predictive Analysis Mechanism Utilizing Grey Wolf Optimizer-Least Squares Support Vector Machines
title_full Rice Predictive Analysis Mechanism Utilizing Grey Wolf Optimizer-Least Squares Support Vector Machines
title_fullStr Rice Predictive Analysis Mechanism Utilizing Grey Wolf Optimizer-Least Squares Support Vector Machines
title_full_unstemmed Rice Predictive Analysis Mechanism Utilizing Grey Wolf Optimizer-Least Squares Support Vector Machines
title_sort rice predictive analysis mechanism utilizing grey wolf optimizer-least squares support vector machines
publisher Asian Research Publishing Network (ARPN)
publishDate 2015
url http://umpir.ump.edu.my/id/eprint/16363/
http://umpir.ump.edu.my/id/eprint/16363/
http://umpir.ump.edu.my/id/eprint/16363/1/PRICE%20PREDICTIVE%20ANALYSIS%20MECHANISM%20UTILIZING%20GREY%20WOLF_ARPN.pdf
first_indexed 2023-09-18T22:21:58Z
last_indexed 2023-09-18T22:21:58Z
_version_ 1777415716456103936

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