Abstract
The education sector has witnessed increasing interest in data-driven decision-making. Education sector requires the use of business intelligence (BI) to ensure the extraction of information allows the educational staff to function more effective. This paper illustrated the use of metaheuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) in BI to ensure the selection of informative features for decision making. Higher education based case studies are discussed to prove that the proposed technique able to improve the decisions and results to select features that able to increase the number of postgraduates in graduating within time allocated. The research aimed to propose a novel method to identify and select informative features. The accuracy for proposed algorithm is ACO in this research is 96.2% while for GA is 83.1% and PSO is 93.3%. Experiments show that using the informative features has better analysis of data.
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Acknowledgments
This research is fully supported by Research University Grant (RUG) under the Vote No. 02G87; Fundamental Research Grant Scheme (FRGS) under the Vote No. 4F783. The authors fully acknowledged the Ministry of Higher Education (MOHE) and Universiti Teknologi Malaysia (UTM) for approved fund which makes this research is viable and effective.
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Othman, M.S., Kumaran, S.R., Yusuf, L.M. (2018). An Implementation of Metaheuristic Algorithms in Business Intelligence Focusing on Higher Education Case Study. In: Saeed, F., Gazem, N., Patnaik, S., Saed Balaid, A., Mohammed, F. (eds) Recent Trends in Information and Communication Technology. IRICT 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-59427-9_51
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DOI: https://doi.org/10.1007/978-3-319-59427-9_51
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