Design of nearest neighbor classifiers: Multi-objective approach

Jian Hung Chen, Hung Ming Chen, Shinn-Ying Ho*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

The goal of designing optimal nearest neighbor classifiers is to maximize classification accuracy while minimizing the sizes of both reference and feature sets. A usual way is to adaptively weight the three objectives as an objective function and then use a single-objective optimization method for achieving this goal. This paper proposes a multi-objective approach to cope with the weight tuning problem for practitioners. A novel intelligent multi-objective evolutionary algorithm IMOEA is utilized to simultaneously edit compact reference and feature sets for nearest neighbor classification. Three comparison studies are designed to evaluate performance of the proposed approach. It is shown empirically that the IMOEA-designed classifiers have high classification accuracy and small sizes of reference and feature sets. Moreover, IMOEA can provide a set of good solutions for practitioners to choose from in a single run. The simulation results indicate that the IMOEA-based approach is an expedient method to design nearest neighbor classifiers, compared with an existing single-objective approach.

Original languageEnglish
Pages (from-to)3-22
Number of pages20
JournalInternational Journal of Approximate Reasoning
Volume40
Issue number1-2
DOIs
StatePublished - 1 Jul 2005

Keywords

  • Feature selection
  • Genetic algorithm
  • Minimum reference set
  • Multi-objective optimization
  • Nearest neighbor classifier

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