Sentence examples for balanced classification from inspiring English sources
The data obtained for the 22 spruce populations at the four sites did not represent a fully balanced classification.
Balanced Classification Rate (BCR) is the average of sensitivity and specificity which may be defined as a measure to test classifiers ability to avoid false classification.
Table 1 Classification results Classifier* Cost TP rate FP Rate ROC area Accuracy BCR# CSC Naïve Bayes 38 54.5 20.1 79.85 6685 66 CSC Random Forest 65000 60.2 19.0 77.3 81.19 70 *CSC denotes CostSensitiveClassifier, # Balanced Classification Rate.
We used various statistical measures such as Accuracy, Sensitivity, Specificity, Balanced Classification Rate (BCR) and Receiver Operating Characteristic (ROC) to evaluate the models.
We show that, an ensemble feature selection and an ensemble classifier, integrated with random subset selection, and a class balancer have the potential to generate more accurate, lower cost, and balanced classification framework.
Figure 2 Comparison of accuracy and balanced classification rate.
In a June 29 , 2012 administrative letter, Tonya Deanes, then deputy chief human capital officer, said that the VA was working to figure out how to "balance classification standards and equal pay for equal work with the potential number of resulting changes to lower grade".
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.
For completely balanced normal classification models, it is well known that REML and ANOVA estimators of variance components are equal.
Goosey-Tolfrey et al. [ 6] who assessed separated wheelchair athletes with or without trunk stability and balance classification found a significant difference between both groups of VO2peak (absolute and relative).
The use of both classification balanced accuracy and prediction balanced accuracy within the GEDT algorithm emphasizes generalizability of the final model.
