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Abstract
We present here an introduction to Brainstorming approach, that was recently proposed
as a consensus meta-learning technique, and used in several practical applications
in bioinformatics and chemoinformatics. The consensus learning denotes heterogeneous
theoretical classification method, where one trains an ensemble of machine learning
algorithms using different types of input training data representations. In the second
step all solutions are gathered and the consensus is build between them. Therefore
no early solution, given even by a generally low performing algorithm, is not discarder
until the late phase of prediction, when the final conclusion is drawn by comparing
different machine learning models. This final phase, i.e. consensus learning, is trying
to balance the generality of solution and the overall performance of trained model.