However, not all reported performance measures accurately reflect how the classifiers would be expected to perform on new samples (see [ 15] for an overview of common problems with gene expression classifiers).
A key feature of the probabilistic approach to classification is that it aims to be coherent about managing uncertainty throughout the analysis, which ultimately provides predictions that accurately reflect the confidence of the classifier decision.
However, error numbers sometime cannot faithfully reflect the performance of weak classifiers because the severity of the error is not always the same.
Recall, specificity and precision reflect the prediction tendencies of classifiers.
Therefore, the present classification accuracies only roughly reflect the quality of our classifiers.
For the CRC set, the improved performance of the new instrument was indeed reflected in the classifiers constructed, as the best classification was obtained with the data set generated by the PCS 4000 instrument, using the total number of peaks detected.
The decision fusion rules, designed primarily based on the classification confidence of the CNN, reflect the generally complementary patterns of the individual classifiers.
Therefore, here we present four feature vectors, which reflect the positional characteristics of the intronic miRNAs in their host genes to train the classifier (Table 1).
In conditions like fadrozole-brain, prochloraz-brain, and prochloraz-ovary in the training data, F-ratio reflected the performance of classifiers (Table 5).
The training variability reflects the stability of the classifier performance when the training set varies, and the testing variability reflects the expected performance variation for different test sets.
