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In one-class classification problems, only the data for the target class isavailable, whereas the data for the non-target class may be completely absent.In this paper, we study one-class nearest neighbour (OCNN) classifiers andtheir different variants. We present a theoretical analysis to show therelationships among different variants of OCNN that may use differentneighbours or thresholds to identify unseen examples of the non-target class.We also present a method based on inter-quartile range for optimisingparameters used in OCNN in the absence of non-target data during training.Then, we propose two ensemble approaches based on random subspace and randomprojection methods to create accurate OCNN ensembles. We tested the proposedmethods on 15 benchmark and real world domain-specific datasets and show thatrandom-projection ensembles of OCNN perform best.

Relationship between Variants of One-Class Nearest Neighbours and Creating their Accurate Ensembles