Sentence examples for methods usually achieve from inspiring English sources

Wrapper methods usually achieve superior results than filter methods.

For more details of these results, this study included the results as an Additional file 3. The proposed methods usually achieve high AUC value.

The empirical study, with different discretization algorithms, classifiers, two types of dataset of standard datasets and imbalanced datasets, suggests that the proposed graph clustering-based methods usually achieve superior discretization results compared to those of the previous well-known discretization algorithms.

While the kernel-based leaning classification methods [for instance, the support vector machines (SVM) method] try to pick out the informative samples for classification, thus these methods usually achieve higher recognition accuracy and at the same time maintain relative sparsity of the support vectors.

As pointed out by TetraMito, the GO-based methods usually achieve better performances, like Fan and Li's work.

The type of methods usually achieved a low capacity with severe distortion.

Nevertheless, we still observe that the method usually achieves the best performance when γ is equal to 1. Therefore, we use γ=1 as the default setting in our method.

Our results show that several methods (structured support vector machine, binary support vector machines and guilt-by-association methods) do not usually achieve the same level of accuracy on these two tasks as that achieved by cross-validation, and that predicting novel annotations for previously annotated proteins is a harder problem than predicting annotations for uncharacterized proteins.

It is known that the methods which use ensemble classifier usually achieve more accurate and robust performance than the methods using single classifier.

The results suggest that the proposed methods can handle with imbalanced datasets, which usually achieve high AUC value than many well-known discretizers.

The results suggest the new approach is able to preserve the natural groups and usually achieve the efficiency in terms of classifier performance, and the desired number of intervals than the comparative methods.