skip to main content
10.1145/1276958.1276985acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
Article

Suppression based immune mechanism to find arepresentative training set in data classification tasks

Published: 07 July 2007 Publication History

Abstract

This article proposes a new classifier {inspired by a biolog-ical immune systems' characteristic{ which also belongs tothe class of k-nearest-neighbors algorithms. Its main fea-ture is a suppression mechanism used to reduce the size of the training set {maintaining the most significative samples{without loosing much capability of generalization.

Reference

[1]
R. P. Espínola and N. F. F. Ebecken. On extending f-measure and g-mean metrics to multi-class problems. In DATA MINING VI - Data Mining, Text Mining and Their Business Applications, volume 1, pages 25--34, 2005.

Index Terms

  1. Suppression based immune mechanism to find arepresentative training set in data classification tasks

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
      July 2007
      2313 pages
      ISBN:9781595936974
      DOI:10.1145/1276958

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 07 July 2007

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. KDD
      2. artificial immune systems
      3. classification

      Qualifiers

      • Article

      Conference

      GECCO07
      Sponsor:

      Acceptance Rates

      GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
      Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • 0
        Total Citations
      • 138
        Total Downloads
      • Downloads (Last 12 months)0
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 16 Feb 2025

      Other Metrics

      Citations

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media