The total classification accuracy by that approach for both the training and testing datasets was 80.31 and 80.05 %, respectively.
While with the feature selection algorithm mRMR and IG, the total classification accuracy of WSVM achieves the best classification accuracy.
Classification accuracy is the overall correctness of the classifier and is calculated as the sum of correct cancer classifications divided by the total number of classifications.
The average total classification accuracy for all subjects was 95.1 %.
The accuracy (Acc) is the overall correctness of the model, and it is calculated as the sum of correct classifications divided by the total number of instances, while the kappa statistic is a chance-corrected measure of agreement between the classifications and the true classes.
Accuracy (total number of correct classifications divided by the total number of sample points) varied in the individual models from 79.2 to 86.6% with an overall accuracy of 82.9%.
By dividing the number of subjects that the classification succeeded for by the total number of subjects, MDR produces a measure of association called an accuracy.
Misclassification error of the CART, compared to the original panel classification, as the gold standard, was calculated as the ratio of the number of indications erroneously classified by the classification tree divided by the total number of indications.
Condemnation rates were calculated by dividing the total number of pairs of lungs condemned under the pneumonic lung classification each month by the total number of slaughtered bovines for each animal class (e.g., calves, cows, heifers and steers).
Cellular Processes and Signalling (31.16%) was found to be the major category from the KOG classifications, of which Signal transduction mechanisms were found to be prominent (11.07% of the total KOG classifications) followed by Post translational modification, protein turnover, chaperones (9.87%) and Intracellular trafficking, secretion and vesicular transport (4.98%).
