Airline revenue management via data mining
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Abstract
Revenue maximisation has been of paramount interest in the airline industry during the past few decades, and numerous studies have been reported, aiming at robust analyses. Principal analysis techniques in most of these studies include computational-based prediction algorithms that are used for a given dataset. In this study, airline specific data, which consists of cabin class passenger data, cabin class supplied capacity data, distance of flights, season, year –month data and revenue data, are analysed using various prediction algorithms. Consistencies and accuracies of different algorithms are compared and reported.
Keywords: Airline industry, airline revenue data, prediction algorithms, Weka, Bayesian network, sequential minimal
optimisation, support vector machines, multilayer perceptron, radial basis function network.
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