Condensed Nearest Neighbor Data Domain Description

A simple yet effective unsupervised classification rule to discriminate between normal and abnormal data is based on accepting test objects whose nearest neighbors´ distances in a reference data set, assumed to model normal behavior, lie within a certain threshold. This work investigates the effect of using a subset of the original data set as the reference set of the classifier. With this aim, the concept of a reference-consistent subset is introduced and it is shown that finding the minimum-cardinality reference-consistent subset is intractable. Then, the condensed nearest neighbor domain description (CNNDD) algorithm is described, which computes a reference-consistent subset with only two reference set passes. Experimental results revealed the advantages of condensing the data set and confirmed the effectiveness of the proposed approach. A thorough comparison with related methods was accomplished, pointing out the strengths and weaknesses of one-class nearest-neighbor-based training-set-consistent condensation.