Sentence examples for methods and classifiers from inspiring English sources

Table 1 shows the average accuracies obtained by combining various feature extraction methods and classifiers to classify the three EEG datasetes, including the normal EEGs (Set A), the interictal EEGs (Set D), and the ictal EEGs (Set E).

The strategy of combining dimensionality reduction methods and classifiers was demonstrated and discussed.

This degrades the performance of many feature extraction methods and classifiers.

The current discriminant analysis method design is generally independent of classifiers, thus the connection between discriminant analysis methods and classifiers is loose.

Compared with the traditional feature extraction methods and classifiers, the proposed models are less likely affected by the imbalance of the dataset.

A comparison of different feature extraction methods and classifiers for the detection of ERPs is performed on a standard data set to determine the best fit for the real time implementation of the BCI.

Various feature selection methods and classifier design strategies have been generally used and compared.

On the other side, to see whether the new methods are superior to others, regression models were built based on average testing accuracy (ms_hr) and highest testing accuracy (hs_hr), respectively, with data set (six benchmark microarray data set), gene selection method (four new methods and six other methods) and classifier (four classification methods) as independent variables.

Classifier ensemble methods and simple classifiers were examined.

In order to obtain an unbiased performance estimate for the SNP selection method and classifier of [8], the above methods were applied by repeated 10-fold cross-validation.

To examine the effects of text classifiers, we show the best classification results for pairs of feature selection methods and text classifiers on the electronic review dataset in Table 3.