The phrase "a correct classification of" is correct and usable in written English.
It can be used when discussing the categorization or organization of items, concepts, or data in a precise manner.
Example: "The study provides a correct classification of various species based on their genetic traits."
Alternatives: "an accurate categorization of" or "a proper classification of".
For depth-three circuits, we obtained a correct classification of about 97%.
The categorization of subjects as normotensive or hypertensive, using the wrist device in subjects without specific training, achieved a correct classification of 84% (with position sensor) and 81% (without position sensor) in comparison to ABPM.
In the clinical test, this sensor module discriminates breaths from the normal subjects, patients with uremia, patients with chronic renal insufficiency (CRI) and patients with chronic renal failure (CRF) well by the discriminating analysis with a correct classification of 86.78%.
Up to now only 40 images with microcalcications from our database have been analyzed, and a CAD tool has been designed to detect clusters, reaching a correct classification of 90%.
At cutoff point 3 of the scale and with a correct classification of subjects of 92.4%, sensitivity, specificity, positive, and negative predictive values were 92, 95, 98, and 79%, respectively.
A threshold was therefore calculated to obtain a hard decision; the best value turned out to be 0.4, which provided a correct classification of 79% of pedestrians and 85% of other objects.
The CAT resulted in a correct classification for the large majority of cases with normal (96%) or clinical scores (89%).
Here, the differences in the acoustic characteristics of stridulations allowed a correct classification rate of 95% of all individuals.
The pattern classification algorithm achieved a correct classification percentage of 92.12% using the information of the entire database.
This method was evaluated against the Fisherface algorithm and exhibited much more stable performance across a wide range of illumination, it alsoachieved a correct classification rate of 70% versus 20% for Fisherface on the equivalent test data.
The model has a correct classification rate of 80% (22 patients misclassified out of 109).
