Binary classification of imbalanced datasets using conformal prediction

[Display omitted] •Conformal Prediction finds a majority of active compounds in highly imbalanced data.•Separate distributions are used for the two classes during classification.•No balancing measures is need for imbalanced data using Conformal Prediction. Aggregated Conformal Prediction is used as an effective alternative to other, more complicated and/or ambiguous methods involving various balancing measures when modelling severely imbalanced datasets. Additional explicit balancing measures other than those already apart of the Conformal Prediction framework are shown not to be required. The Aggregated Conformal Prediction procedure appears to be a promising approach for severely imbalanced datasets in order to retrieve a large majority of active minority class compounds while avoiding information loss or distortion.