data envelopment analysis (dea) is a widely recognized approach for evaluating the efficiencies of decision making units (dmus). because of easy and successful application and case studies, dea has gained much attention and widespread use by business and academy researchers. the conventional dea models (e.g. bcc and ccr) make an assumption that input and output data are exact values on a ratio ...

Gaussian discriminant analysis is a popular classification model, that in the precise case can produce unreliable predictions of high uncertainty (e.g., due to scarce or noisy data). While imprecise probability theory offers nice theoretical framework solve such issues, it has not been yet applied analysis. This work remedies this, by proposing new based on robust Bayesian and near-ignorance pr...