Dual neural gas based structure ensemble with the bagging technique

Hantao Chen, Xiaodong Zhang, Jia You, Guoqiang Han, Le Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

It is widely accepted that cluster ensemble can improve accuracy, stableness and robustness when compared with single cluster approach. As the bagging technique can enhance the prediction accuracy of unstable learning algorithms, and the neural gas algorithm can achieve the structure of datasets, we propose a new structure ensemble framework, named as dual neural gas based structure ensemble with the bagging technique. Experiments on both UCI datasets and synthetic datasets show that tne new framework works well.
Original languageEnglish
Title of host publicationProceedings of 2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012
Pages1400-1405
Number of pages6
Volume4
DOIs
Publication statusPublished - 31 Dec 2012
Event2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012 - Xian, Shaanxi, China
Duration: 15 Jul 201217 Jul 2012

Conference

Conference2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012
Country/TerritoryChina
CityXian, Shaanxi
Period15/07/1217/07/12

Keywords

  • Bagging
  • Neural gas
  • Normalized mutual information
  • Purity
  • Structure ensemble

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Human-Computer Interaction

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