Mustaffa, Zuriani and Yusof, Yuhanis and Kamaruddin, Siti Sakira (2014) An enhanced artificial bee colony optimizer for predictive analysis of heating oil prices using least squares support vector machines. In: Biologically-Inspired Techniques for Knowledge Discovery and Data Mining. IGI Global, pp. 149-173. ISBN 9781466660786

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Abstract

As energy fuels play a significant role in many parts of human life, it is of great importance to have an effective price predictive analysis. In this chapter, the hybridization of Least Squares Support Vector Machines (LSSVM) with an enhanced Artificial Bee Colony (eABC) is proposed to meet the challenge.The eABC, which serves as an optimization tool for LSSVM, is enhanced by two types of mutations, namely the Levy mutation and the conventional mutation.The Levy mutation is introduced to keep the model from falling into local minimum while the conventional mutation prevents the model from over-fitting and/or under-fitting during learning.Later, the predictive analysis is followed by the LSSVM.Realized in predictive analysis of heating oil prices, the empirical findings not only manifest the superiority of eABC-LSSVM in prediction accuracy but also poses an advantage to escape from premature convergence.

Item Type:	Book Section
Additional Information:	Chapter 7
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	School of Computing
Depositing User:	Dr. Yuhanis Yusof
Date Deposited:	02 Sep 2015 01:21
Last Modified:	23 May 2016 07:30
URI:	https://repo.uum.edu.my/id/eprint/15354

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