Building graph-based classifier ensembles by random node selection

Adam Schenker, Horst Bunke, Mark Last, Abraham Kandel

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

11 Scopus citations

Abstract

In this paper we introduce a method of creating structural (i.e. graph-based) classifier ensembles through random node selection. Different k-Nearest Neighbor classifiers, based on a graph distance measure, are created automatically by randomly removing nodes in each prototype graph, similar to random feature subset selection for creating ensembles of statistical classifiers. These classifiers are then combined using a Borda ranking scheme to form a multiple classifier system. We examine the performance of this method when classifying a web document collection; experimental results show the proposed method can outperform a single classifier approach (using either a graph-based or vector-based representation).

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsFabio Roli, Josef Kittler, Terry Windeatt
PublisherSpringer Verlag
Pages214-222
Number of pages9
ISBN (Print)3540221441, 9783540221449
DOIs
StatePublished - 1 Jan 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3077
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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