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...

Acquiring precise data is expensive. Hence, there is interest in the development of computer algorithms that make full use of imprecise data, even though these new algorithms may be more complex and require more resources. In particular, most Genetic Fuzzy Systems (GFS). accept crisp inputs, but changes can be effected to them so that rules are obtained from vague data. Furthermore, the rule ge...