One-Class Classification by Ensembles of Random Planes (OCCERPs)

Algorithm 1

OCCER algorithm.

	Input- An -dimensional training dataset T.
	Output- Outlier score of a data point, C.
	Begin
	Training phase
	Normalise data compute z-score for each feature value using the mean and standard deviation for each feature)
	for i = 1… do
	Select a random pair (R, S) of points from T
	Create a hyperplane, , perpendicular to the line segment between R and S and running through the middle point, , of R and S.
	end for
	Testing phase
	To compute the outlier score of a data point, C, normalise the data point using the steps as the training data points are normalised.
	for i = 1… do
	Compute outlier score by computing the distance between and the projected point of C on hyperplane using the method discussed in Section 3.
	end for
	Combine M outlier scores by minimum approach discussed in Section 3.1 to get the final outlier score.
	End