Nearest neighbor method based on local distribution for classification

Chengsheng Mao, Bin Hu*, Philip Moore, Yun Su, Manman Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

The k-nearest-neighbor (kNN) algorithm is a simple but effective classification method which predicts the class label of a query sample based on information contained in its neighborhood. Previous versions of kNN usually consider the k nearest neighbors separately by the quantity or distance information. However, the quantity and the isolated distance information may be insufficient for effective classification decision. This paper investigates the kNN method from a perspective of local distribution based on which we propose an improved implementation of kNN. The proposed method performs the classification task by assigning the query sample to the class with the maximum posterior probability which is estimated from the local distribution based on the Bayesian rule. Experiments have been conducted using 15 benchmark datasets and the reported experimental results demonstrate excellent performance and robustness for the proposed method when compared to other state-of-the-art classifiers.

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD 2015, Proceedings
EditorsTu-Bao Ho, Hiroshi Motoda, Hiroshi Motoda, Ee-Peng Lim, Tru Cao, David Cheung, Zhi-Hua Zhou
PublisherSpringer Verlag
Pages239-250
Number of pages12
ISBN (Print)9783319180373
DOIs
StatePublished - 2015
Externally publishedYes
Event19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015 - Ho Chi Minh City, Viet Nam
Duration: May 19 2015May 22 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9077
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015
CountryViet Nam
CityHo Chi Minh City
Period5/19/155/22/15

Keywords

  • Classification
  • Local distribution
  • Nearest neighbors
  • Posterior probability

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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