Issue No. 10 - October (2007 vol. 29)
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 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.
classification, data domain description, data condensation, nearest neighbor rule, novelty detection
F. Angiulli, "Condensed Nearest Neighbor Data Domain Description," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 29, no. , pp. 1746-1758, 2007.