An implementation of metaheuristic algorithms in business intelligence focusing on higher education case study

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.