Sentence examples for dataset balancing from inspiring English sources

From this point of view, tables have to be exploited to understand how much the performance of the various configurations of descriptor, dataset balancing, and scaling strategies are influenced by pose and resolution changes.

The average correct estimations (taking into account all the possible combinations of data scaling and dataset balancing) corresponding to each descriptor were LBP 71.5 %, HOG 56.1 %, SWLD 62.6 %, and CLBP 72.4%%.

This trend was also confirmed if the average of correct estimations (taking into account all the possible combinations of data scaling and dataset balancing) corresponding to each descriptor is considered.

Concerning the opportunity to scale in the range [0,1], the variables of the feature vectors and experiments evidenced that, taking into account all the possible descriptors and combinations of dataset balancing, scaled data gave the best average correct estimations (67.0 %).

With regard to the opportunity to scale in the range [0,1], the features in input to the SVM and the experiments evidenced that non-scaled data better preserved the embedded information: the average correct estimations (taking into account all the possible descriptors and combinations of dataset balancing) were 92.3 % in the case of non-scaled input and 85.4 % in the case of scaled input.

Next, we tested the predictive power of divergence in three-way classification on a dataset balanced to have equal class frequency (Table 10).

A threshold of 4 ha for lakes was the best trade-off between having as many lakes as possible included in the census dataset balanced against minimizing errors for extrapolation purposes as we describe in Additional file 9. We describe how we georeferenced the lake sampling location from monitoring and research programs to a lake polygon in the NHD in Additional file 15.

Some of them have been previously reported by independent studies using the same or similar datasets: balancing selection in IL1F5, IL1F7, IL1F10 and IL18RAP [ 13] and positive selection in the TLR1- TLR6- TLR10 region finally attributed to TLR1 [ 38].

Regarding the global properties of the datasets, d-Confidence performed clearly better than confidence on "well behaved" datasets (balanced, collinear, isomorphic and separable).

Existing metagenomic taxonomic classification algorithms, however, do not scale well to analyze large metagenomic datasets, and balancing classification accuracy with computational efficiency presents a fundamental challenge.

Once all prefiltering was complete, data were randomly split into training (two-thirds) and test (one-thirds) datasets while balancing for study of origin and number of relapses with 10-year follow-up.