Recent advances in data mining allow the automatic recognition of physical phenomena in the databases of fusion devices without human intervention. This is important to create large databases of physical events (thereby increasing the statistical relevance) in an unattended manner. Important examples are the L/H and H/L transitions. In this contribution, a novel technique is introduced to automatically locate H/L transitions in JET by using conformal predictors. The focus is on H/L transitions because typically there is not a clear signature in the time series of the most widely available signals to recognize the change of confinement. Conformal predictors hedge their prediction by means of two parameters: confidence and credibility. The technique has been based on binary supervised classifiers to separate the samples of the respective confinement modes. Results with several underlying classifiers are presented.