Nearest neighbor classifier based on nearest feature decisions

Alex Pappachen James, Sima Dimitrijev

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


High feature dimensionality of realistic datasets adversely affects the recognition accuracy of nearest neighbor (NN) classifiers. To address this issue, we introduce a nearest feature classifier that shifts the NN concept from the global-decision level to the level of individual features. Performance comparisons with 12 instance-based classifiers on 13 benchmark University of California Irvine classification datasets show average improvements of 6 and 3.5% in recognition accuracy and area under curve performance measures, respectively. The statistical significance of the observed performance improvements is verified by the Friedman test and by the post hoc Bonferroni-Dunn test. In addition, the application of the classifier is demonstrated on face recognition databases, a character recognition database and medical diagnosis problems for binary and multi-class diagnosis on databases including morphological and gene expression features.

Original languageEnglish
Pages (from-to)1072-1087
Number of pages16
JournalComputer Journal
Issue number9
Publication statusPublished - Sep 2012


  • classification
  • local features
  • local ranking
  • nearest neighbors

ASJC Scopus subject areas

  • Computer Science(all)

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