Abstract
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 language | English |
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Pages (from-to) | 1072-1087 |
Number of pages | 16 |
Journal | Computer Journal |
Volume | 55 |
Issue number | 9 |
DOIs | |
Publication status | Published - Sep 2012 |
Keywords
- classification
- local features
- local ranking
- nearest neighbors
ASJC Scopus subject areas
- Computer Science(all)