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• W2733300139 abstract "In accounting and finance domains, bankruptcy prediction is of great utility for all of the economic stakeholders. The challenge of accurate assessment of business failure prediction, specially under scenarios of financial crisis, is known to be complicated. Although there have been many successful studies on bankruptcy detection, seldom probabilistic approaches were carried out. In this paper we assume a probabilistic point-of-view by applying Gaussian processes (GP) in the context of bankruptcy prediction, comparing it against the support vector machines (SVM) and the logistic regression (LR). Using real-world bankruptcy data, an in-depth analysis is conducted showing that, in addition to a probabilistic interpretation, the GP can effectively improve the bankruptcy prediction performance with high accuracy when compared to the other approaches. We additionally generate a complete graphical visualization to improve our understanding of the different attained performances, effectively compiling all the conducted experiments in a meaningful way. We complete our study with an entropy-based analysis that highlights the uncertainty handling properties provided by the GP, crucial for prediction tasks under extremely competitive and volatile business environments." @default.
• W2733300139 created "2017-07-14" @default.
• W2733300139 creator A5000969446 @default.
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• W2733300139 date "2017-11-01" @default.
• W2733300139 modified "2023-10-16" @default.
• W2733300139 title "Probabilistic modeling and visualization for bankruptcy prediction" @default.
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• W2733300139 doi "https://doi.org/10.1016/j.asoc.2017.06.043" @default.
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