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Based on this result, it would seem natural to search for another voting procedure that differs from the majority method in not giving rise to this form of cycles.
However, for purposes of this preliminary study, using the majority method seemed sufficient.
This is because the Average method uses real values output from the individual models, while the Majority method uses voting.
In contrast, the majority method considers all classifiers equally relevant and takes a vote, deciding for class that agrees with the majority.
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The majority methods for drug/compound target prediction are based on chemical structure, including the chemogenomic approach.
The results showed that the suggested method outperformed conventional fusion of the features and classification units using the majority voting method.
Finally, it fuses the classifiers using the majority vote method.
Therefore, at least on this expression dataset, the performance of the majority rule method is similar to the SVM.
The final category index is derived using the majority voting method.
The ensemble based SLFNs structure combined with the majority voting method is then proposed for protein sequence classification.
Second, the ensemble based SLFNs network structure is proposed and the majority voting method is adopted to further enhance the protein sequence classification performance.
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