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.

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Simulation approaches constitute a well-established tool to model, understand and predict the behavior of transportation systems, and ultimately to assess the performance of transportation policies. Due to their multidimensionality nature, such systems are not often approachable through conventional analytic methods, making simulation modeling the only reliable tool of study. Nevertheless, simulation models can turn out to be computationally expensive when embedded with enough detail. An immediate answer to this shortcoming is the use of simulation metamodels that are designed to approximate the simulators' results.In this work, the authors propose a metamodeling approach based on active learning that seeks to improve the exploration process of the simulation input space and the associated output behavior. A Gaussian Process (GP) is used as a metamodel to approximate the function inherently defined by the simulation model itself. The GPs are able to nicely handle the uncertainty associated with their predictions, which eventually can be improved with active learning through simulation runs. This provides a practical and efficient way to analyze the simulator's behavior and therefore to assess the performance of policies regarding the underlying real-world systems and services.The authors illustrate the proposed methodology using an Emergency Medical Service (EMS) simulator. Two outputs are analyzed and compared, namely, the survival rate and response time averages. The medical emergency response time recommendation of eight minutes is explored as well its relation with the survival rate. The results show that this methodology is able to identify regions in the simulation input space that might affect the performance and success of medical policies with regards to emergency vehicle dispatching services.

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