Weighted K-Nearest Neighbor Revisited

M. Bicego, Marco Loog

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

8 Citations (Scopus)

Abstract

In this paper we show that weighted K-Nearest Neighbor, a variation of the classic K-Nearest Neighbor, can be reinterpreted from a classifier combining perspective, specifically as a fixed combiner rule, the sum rule. Subsequently, we experimentally demonstrate that it can be rather beneficial to consider other combining schemes as well. In particular, we focus on trained combiners and illustrate the positive effect these can have on classification performance.
Original languageEnglish
Title of host publication2016 23rd International Conference on Pattern Recognition (ICPR)
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages1642-1647
Number of pages6
ISBN (Electronic)978-1-5090-4847-2
ISBN (Print)978-1-5090-4848-9
DOIs
Publication statusPublished - 2016
EventICPR 2016: 23rd International Conference on Pattern Recognition - Cancún, Mexico
Duration: 4 Dec 20168 Dec 2016
Conference number: 23

Conference

ConferenceICPR 2016
CountryMexico
CityCancún
Period4/12/168/12/16

Keywords

  • Training
  • Diversity reception
  • Pattern recognition
  • Electronic mail
  • Testing
  • Degradation
  • Terminology

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