IKNN: Informative K-nearest neighbor pattern classification

Song Yang, Huang Jian, Zhou Ding, Zha Hongyuan, C. Lee Giles

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

62 Scopus citations

Abstract

The K-nearest neighbor (KNN) decision rule has been a ubiquitous classification tool with good scalability. Past experience has shown that the optimal choice of K depends upon the data, making it laborious to tune the parameter for different applications. We introduce a new metric that measures the informativeness of objects to be classified. When applied as a query-based distance metric to measure the closeness between objects, two novel KNN procedures, Locally Informative-KNN (LI-KNN) and Globally Informative-KNN (GI-KNN), are proposed. By selecting a subset of most informative objects from neighborhoods, our methods exhibit stability to the change of input parameters, number of neighbors(K) and informative points (I). Experiments on UCI benchmark data and diverse real-world data sets indicate that our approaches are application-independent and can generally outperform several popular KNN extensions, as well as SVM and Boosting methods.

Original languageEnglish (US)
Title of host publicationKnowledge Discovery in Database
Subtitle of host publicationPKDD 2007 - 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings
Pages248-264
Number of pages17
StatePublished - Dec 1 2007
Event11th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2007 - Warsaw, Poland
Duration: Sep 17 2007Sep 21 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4702 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other11th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2007
CountryPoland
CityWarsaw
Period9/17/079/21/07

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Yang, S., Jian, H., Ding, Z., Hongyuan, Z., & Giles, C. L. (2007). IKNN: Informative K-nearest neighbor pattern classification. In Knowledge Discovery in Database: PKDD 2007 - 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings (pp. 248-264). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4702 LNAI).