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The Positive subset contained the majority class samples where its neighbors have the sample class label, the Minority subset contained the minority samples, and the Boundary subset contained the minority samples that have any majority class sample in its neighbors.
The final model is then used to predict the class samples of the test set.
The results indicate that the first seven stages rejected 98%% of the non-current class samples.
Additionally, the applicability of these models outside the environmental conditions or size class sampled is unknown.
The green circles and the red triangles represent two different class samples respectively.
Technique Di: data set having 'N' instances; Dmj: majority class samples am (m = 1, 2, …, m); Dmn: minority class samples bn (n = 1, 2, …, n); i: iteration count (1); Cim: intermediate clusters; Do: a set of synthetic positive instances.
It means the AdaBoostRBFSVM classifier is indeed usable and could largely reduce the number of normal class samples being misclassified.
It assigns the weight to each of them based on Euclidean distance from the nearest majority class samples.
The "Reading" class samples represent visual patterns of open books in FPV images, as shown in Fig. 2b.
It means that the LSVM classifier can decrease the number of minority class samples that are misclassified.
It uses a weighed distribution for different minority class samples according to their levels of difficulty in learning.
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