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,...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Asian Research Publishing Network (ARPN)
2015
|
| Subjects: | |
| Online Access: | 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 |
| _version_ | 1848820174844592128 |
|---|---|
| author | Zuriani, Mustaffa M. H., Sulaiman |
| author_facet | Zuriani, Mustaffa M. H., Sulaiman |
| author_sort | Zuriani, Mustaffa |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T02:05:16Z |
| format | Article |
| id | ump-16363 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:05:16Z |
| publishDate | 2015 |
| publisher | Asian Research Publishing Network (ARPN) |
| recordtype | eprints |
| repository_type | Digital Repository |
| 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. (Published) http://www.arpnjournals.org/jeas/research_papers/rp_2015/jeas_1215_3198.pdf |
| spellingShingle | QA75 Electronic computers. Computer science Zuriani, Mustaffa M. H., Sulaiman Rice Predictive Analysis Mechanism Utilizing Grey Wolf Optimizer-Least Squares Support Vector Machines |
| title | 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_short | 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 |
| topic | QA75 Electronic computers. Computer science |
| 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 |