Distance metrics for instance-based learning

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

12 Scopus citations


Instance-based learning techniques use a set of stored training instances to classify new examples. The most common such learning technique is the nearestneighbor method, in which new instances are classified according to the closest training instance. A critical element of any such method is the metric used to determine distance between instances. Euclidean distance is by far the most commonly used metric; no one, however, has systematically considered whether a different metric, such as Manhattan distance, might perform equally well on naturally occurring data sets. Some evidence from psychological research indicates that Manhattan distance might be preferable in some circumstances. This paper examines three different distance metrics and presents experimental comparisons using data from three domains: malignant cancer classification, heart disease diagnosis, and diabetes prediction. The results of these studies indicate that the Manhattan distance metric works works quite well, although not better than the Euclidean metric that has become a standard for machine learning experiments. Because the nearest neighbor technique provides a good benchmark for comparisons with other learning algorithms, the results below include a number of such comparisons, which show that nearest neighbor, using any distance metric, compares quite well to other machine learning techniques.

Original languageEnglish (US)
Title of host publicationMethodologies for Intelligent Systems - 6th International Symposium, ISMIS 1991, Proceedings
EditorsZbigniew W. Ras, Maria Zemankova
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783540545637
StatePublished - 1991
Event6th International Symposium on Methodologies for Intelligent Systems, ISMIS 1991 - Charlotte , United States
Duration: Oct 16 1991Oct 19 1991

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume542 LNAI Part F2
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other6th International Symposium on Methodologies for Intelligent Systems, ISMIS 1991
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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