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      BRAINSTORMING: Consensus Learning in Practice

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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.

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          Author and article information

          Journal
          2009-10-06
          Article
          0910.0949
          ae090c19-d187-42e8-9998-d6e8e95a5b14

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          Custom metadata
          14 pages, 1 figure
          stat.ML stat.ME

          Machine learning,Methodology
          Machine learning, Methodology

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