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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).
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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].
This risk function is formulated as an expected loss of classifying a test instance as a particular class thus we want to minimize the risk function as to have a higher probability of correct classification.
The testing phase classifies a test instance as normal or anomalous, using the classifier.
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.
A test instance is considered anomalous if it is not classified as normal by any of the classifiers.
Classification [9, 10] is used to learn a model called classifier from a set of labeled data instances called training and then to classify a test instance into one of the classes using the learned model known as testing.
Suppose a test instance y will be classified.
If a PPI (test instance) was judged as positive in a process, we calculated similarities between the feature vector of the test and that of each positive.
Finally, the average of these classifiers scores is taken as the final prediction score for a particular test instance.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com