DIVACE: Diverse and accurate ensemble learning algorithm

Arjun CHANDRA, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

62 Citations (Scopus)

Abstract

In order for a neural network ensemble to generalise properly, two factors are considered vital. One is the diversity and the other is the accuracy of the networks that comprise the ensemble. There exists a tradeoff as to what should be the optimal measures of diversity and accuracy. The aim of this paper is to address this issue. We propose the DIVACE algorithm which tries to produce an ensemble as it searches for the optimum point on the diversity-accuracy curve. The DIVACE algorithm formulates the ensemble learning problem as a multi-objective problem explicitly. © Springer-Verlag Berlin Heidelberg 2004.
Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning : IDEAL 2004 : 5th International Conference, Exeter, UK, August 25-27, 2004, Proceedings
EditorsZheng Rong YANG, Hujun YIN, Richard M. EVERSON
PublisherSpringer Berlin Heidelberg
Pages619-625
Number of pages7
ISBN (Electronic)9783540286516
ISBN (Print)9783540228813
DOIs
Publication statusPublished - 2004
Externally publishedYes
Event5th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2004 - Exeter, United Kingdom
Duration: 25 Aug 200427 Aug 2004

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin, Heidelberg
Volume3177
ISSN (Print)3029-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2004
Country/TerritoryUnited Kingdom
CityExeter
Period25/08/0427/08/04

Keywords

  • Mutual Information
  • Pareto Front
  • Ensemble Member
  • Member Network
  • Neural Computation

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