In rough set theory, the approximation quality γ is the traditional measure to evaluate the classification success of attributes in terms of a numerical evaluation of the dependency properties generated by these attributes.
Finally, the test accuracy of AQI class was calculated to evaluate the classification performance.
First of all, we evaluate the classification accuracy of the proposed approach.
An error matrix was prepared using ground truthing data to evaluate the classification accuracy.
We survey various IaaS systems and map them onto our taxonomy to evaluate the classification.
To evaluate the classification performance at the image level, we report the corresponding confusion matrix in Table 5.
To evaluate the classification accuracy of this scheme for different users and different attack scenarios, we build two models.
By choosing a fixed classifier (1-NN), we can evaluate the classification performance of different distance metrics over different datasets.
The classification is made over the segments extracted with the ground truth boundaries to evaluate the classification accuracy over the whole segment.
The datasets generated from the Tennessee Eastman process (TEP) simulator were used to evaluate the classification performance.
