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There is a test instance (white color) that will be classified.
Outliers If x is a test instance and if doesn't match the specifications of any of the class 'C' of the model Mi then 'x' is an outlier of the model Mi.
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That is, given a test instance T i and a training set S, and a large number of classifiers trained from bags of m samples randomly drawn with repetitions from S. Would more than 50% of these classifiers predict T i to be of the positive class label?
After the training process has been completed, a test instance can be classified by assigning it the class label of the nearest neuron.
The BBFR score R j of the feature j is defined as a sum of r j (i) over all bootstrap runs as follows: (3) R j = ∑ i = 1 L B r j (i ) The maximum possible value of the BBFR score is LB N. Let q k be the number of bootstrap iterations where the observation k is chosen as a test instance (not a member of the bootstrap sample).
A test instance is considered anomalous if it is not classified as normal by any of the classifiers.
A test instance is run on all the decision trees grown in the forest.
P (L = 1) actually reflects a "probability" that a test instance is classified as class 1.
This theory regards an instance as a point in synthesis space; thus, the label of a test instance is probably similar to those of several nearest points.
The predictions of every subclassifier in the ensemble on a test instance are combined via averaging, and the resulting probabilities represent the final class distribution of the instance.
Once we set up a threshold value as the boundary, a test instance can be assigned a class label at each of the root, child and grandchild node (when N = 3) in RMCT classifier [ 8- 10].
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CEO of Professional Science Editing for Scientists @ prosciediting.com