 Prediction of hotel bankruptcy using support vector machine, artificial neural network, logistic regression, and multivariate discriminant analysisSoo Y. Kim The Service Industries Journal, 2008, vol. 31, issue 3, 441-468 Abstract: The objectives of this paper are firstly, to provide an optimal hotel bankruptcy prediction approach to minimize the empirical risk of misclassification and secondly, to investigate the functional characteristics of multivariate discriminant analysis, logistic, artificial neural networks (ANNs), and support vector machine (SVM) models in hotel bankruptcy prediction. The performances were evaluated not only in terms of overall classification and prediction accuracy but also in terms of relative error cost ratios. The results showed that ANN and SVM were very applicable models in bankruptcy prediction with data from Korean hotels. When jointly measuring both type I and type II errors, especially allowing for the greater costs associated with type I errors, however, ANN was more accurate with smaller estimated relative error costs than SVM. Thus, if the objective is to find the best early warning technique that performs accurately with small relative error costs, then, it will be worth considering ANN method for hotel bankruptcy prediction. Date: 2008 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:servic:v:31:y:2008:i:3:p:441-468 Ordering information: This journal article can be ordered from DOI: 10.1080/02642060802712848 Access Statistics for this article The Service Industries Journal is currently edited by Eileen Bridges, Professor Domingo Ribeiro, Ronald Goldsmith, Barry Howcroft and Youjae Yi More articles in The Service Industries Journal from Taylor & Francis Journals |
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