A counterexample to Tomek's consistency theorem for a condensed nearest neighbor decision rule

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The condensed nearest neighbor rule (CNN) was proposed by Hart (1968) as a method to reduce the storage requirements of the original data set D for the efficient implementation of the nearest neighbor decision rule in pattern classification problems. Tomek (1976a) suggested two modifications of CNN in order to improve its performance. As a first step in Tomek's second method he computes a subset C of D, for subsequent use in CNN, and claims that C is training-set-consistent, i.e., that all data points in D are correctly classified by the nearest neighbor rule using C. In this note we provide a counterexample to this claim. We also analyze Tomek's algorithm in the context of more recent graph-theoretical condensing schemes.

Original languageEnglish (US)
Pages (from-to)797-801
Number of pages5
JournalPattern Recognition Letters
Issue number8
StatePublished - Aug 1994


ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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