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To circumvent these problems and boost the reliability of the classifier we used a consensus prediction approach, which requires a gene to be classified as CM in multiple classification runs that use different dataset definitions.
It shows the reliability of the classifier.
For the sample dataset, the current training dataset is small, which may affect the reliability of the classifier.
These results further confirmed the reliability of the classifier.
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The reliability of the network classifier, as well as general drawbacks of the methods used, are discussed.
Classifiers were developed with the training set, and the reliability of these classifiers was examined with the test set.
Moreover, the inverted use of the classifier results, where correct classifications are used to assess the reliability of the misclassifications, puts automatic classifiers in a new perspective that could provide more interesting applications in the future.
It reflects the reliability of the evaluation of our classifier.
In this study, kNN algorithm was also used as a classifier for increasing the reliability of the results obtained by MLPNN.
Finally, we used a multivariate classifier (linear CSVMC) to further investigate the reliability of the topography of PRC vs PHC preferential connectivity within the EC.
To improve the reliability of the score, data cleaning, iterative under-sampling, and the ensemble approach were combined with a Random Forest classifier.
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