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Test samples are classified by assigning the class labels using k nearest training samples.
ANNC exploits "representation-based distance" to select the nearest training samples of new fault sample and identifies fault type in a weighting strategy.
Moreover, the number of nearest training samples of each new fault sample is adaptively determined according to the density of the local distribution of the new fault sample.
This strategy achieves a compromise between maximizing the distance between hyperplane and nearest training samples and minimizing the separating error.
The hyperplane that maximizes the margin (i.e., the distance from the nearest training points) is selected by the SVM.
In addition, each test/new sample will be associated with the number K j of nearest training neighbours for which the previous condition holds true.
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