Sentence examples for performance of each classifier from inspiring English sources

The performance of each classifier was assessed by two measures: (i) Percentage of samples correctly classified (Accuracy) and (ii) F measure, which is the harmonic mean of precision and recall.

The performance of each classifier is presented as the percentage of test set examples which are correctly classified.

There are significant similarities in the performance of each classifier.

The performance of each classifier is also evaluated using 5-fold cross validation.

The number of PCs was determined based on the best performance of each classifier [18].

To test the performance of each classifier, we employed a k-fold cross validation approach (Refaeilzadeh et al., 2009).

For example, based on Table 7, using area under the ROC curve (AUC) or accuracy as a performance measure of each classifier indicates that ISC and cosine similarity are in the same group so their performance are not significantly different from each other.

This result can be easily explained by the following: extracting a too-small subgroup of feature genes can negatively affect the performance of each base classifier, whereas using too many feature genes can negatively affect diversity among base classifiers.

We also used ROC (Receiver Operating Characteristic) curves, PR (Precision-Recall) curves, and the AUC (area under the curve) to evaluate the performance of each constructed classifier.

Therefore, it contributed to the lack of successful performance of each single source classifier on the other source (Table 2A).

In addition, we evaluated the performance of each of the classifiers derived from the pooled data on an independent large validation set of 2000 artificial samples, see Figure 4A and 4B. Figure 4A and 4B show that a high degree of synergy is obtained by pooling artificial datasets.