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Bayesian networks have been widely used for classification problems. These models, structure of the network and/or its parameters (probability distributions), are usually built from a data set. Sometimes we do not have information about all the possible values of the class variable, e.g. data about a reactor failure in a nuclear power station. This problem is usually focused as an anomaly detection problem. Based on this idea, we have designed a decision support system tool of general purpose.