Article Type

Research Article

Publication Title

IEEE Access


A novel open set classifier is presented in this work, where the neighborhood of a test instance is determined using the principles of Reverse k -nearest neighbors ( Rk NN). The Rk NN count of an instance can have any non-negative value less or equal to the size of the training set. While dealing with an open dataset, consisting of known and unknown classes, the zero count can provide a possible solution for detecting the unknown class. Positive Rk NN count along with the nearest Rk NN distance information are used to determine the known class classifications. Experiments are carried out on ten real world datasets, with various openness values on five state-of-the-art open set learners and the proposed scheme. Their performance is measured on three evaluating metrics namely accuracy, average F1 over known and unknown classes, and Known class F1. Empirical results indicate comparable to superior performance delivered by the proposed method over the state-of-the-art approaches on all but one dataset.

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Open Access, Gold