Mustaffa, Zuriani and Yusof, Yuhanis and Kamaruddin, Siti Sakira (2014) Enhanced artificial bee colony for training least squares support vector machines in commodity price forecasting. Journal of Computational Science, 5 (2). pp. 196-205. ISSN 1877-7503

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Abstract

The importance of optimizing machine learning control parameters has motivated researchers to investigate for proficient optimization techniques.In this study, a Swarm Intelligence approach, namely artificial bee colony (ABC) is utilized to optimize parameters of least squares support vector machines.Considering critical issues such as enriching the searching strategy and preventing over fitting, two modifications to the original ABC are introduced. By using commodities prices time series as empirical data, the proposed technique is compared against two techniques, including Back Propagation Neural Network and by Genetic Algorithm.Empirical results show the capability of the proposed technique in producing higher prediction accuracy for the prices of interested time series data.

Item Type:	Article
Uncontrolled Keywords:	Artificial bee colony; Commodity price forecasting; Least squares support vector machines; Levy probability distribution
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	School of Computing
Depositing User:	Dr. Yuhanis Yusof
Date Deposited:	01 Sep 2015 08:39
Last Modified:	23 May 2016 07:23
URI:	https://repo.uum.edu.my/id/eprint/15345

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