Sentence examples for samples for feature from inspiring English sources
Since we use the combined set of both 3' and 5' samples for feature selection, the most informative position-specific features as shown in Figure 8C lie symmetrically in both ends of the flanking regions surrounding the mature miRNA.
The normalization is, g ˆ i, j = g i, j sd g i + s d 10 g where g i,j) is the value for feature i in sample j, sd(g(i)) is the standard deviation across samples for feature i, sd10(g) is the 10-percentile value of standard deviations across features and g ^ i, j is the normalized feature value.
Unlike cross-validation, wherein the test set is drawn from the same population as that of the training set, an independent sample is one that is completely separate from the set of samples used for feature selection and model building.
We imputed remaining missing measurements with the median across samples for each feature.
Third, we removed any molecular features measurements that were missed from 3%% or more samples and replaced missing values with the median across all samples for each feature.
For each example, we considered the impact of varying the number of observations (samples) for the features from 50 (a reasonable value for many "omics" approaches) to 500.
More specifically, we simulate two groups of three samples for 960 features from Gaussian distributions with standard deviations equalling those of the corresponding data estimates.
The danger is that these large changes could increase the variance across samples for individual features, rather than reducing it as desired.
The new synthetic samples for continuous features are generated through the following steps [ 12]: Step 1: For each instance in the minority class, compute the distance between a feature vector of the instance and one of its k nearest neighbors.
Figure 6 Average recognition rates of STL TSB-HMMs versus the number of training samples for time domain feature and spectrogram feature.
A structure is developed for the detection of epileptic seizure EEG signals based on sampling for the feature extraction stage by proposing optimum allocation sampling technique (OAT).
