Sentence examples for decrease classification from inspiring English sources

Districts are putting more disabled students into regular classrooms, a practice called mainstreaming, which research has shown tends to decrease classification rates.

Results show that multi-temporal datasets decrease classification uncertainty for different crops compared to single data sets, but there was no "one-image-combination-fits-all" solution.

However, it relies on the assumption that all features are equally important, which may decrease classification performance when dealing with high-dimensional and noisy data.

However, genes with poor or no discriminating power behave as noise and usually decrease classification accuracy substantially.

~Omics data also needs to be preprocessed for highly correlated features, as the contribution of such features in classification would be wrongly estimated in tree-based classifiers [ 5], and for features with homogeneous values across all observations as they decrease classification accuracy [ 6].

Given the joint feature-label distribution, increasing the number of features always results in decreased classification error; however, this is not the case when a classifier is designed via a classification rule from sample data.

Frequently, strains with an ambiguous phenotype like 'Maybe' have been misclassified, thereby decreasing classification accuracy.

In a demonstration of their technique aimed at web page classification, the addition of unlabeled samples decreased classification error relative to classification using only labeled data.

To further explore how these features relate to H2A/H4R3me2s, we built a single classification tree [ 28], which, compared to the random forests ensemble of trees, may more readily reveal interpretable rules, albeit at the cost of decreased classification accuracy.

Gschwandtner et al. showed that most feature extraction methods evaluated failed to take advantage of applying distortion correction as a pre-processing step to the endoscopic images, resulting in a decreased classification accuracy.

Also, many other motif discovery algorithms and discrimination methods have been proposed before, but they typically come at the price of decreased classification accuracy as compared to SVMs with exhaustive kernels.