On semi-supervised clustering via multiobjective optimization

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Semi-supervised classification uses aspects of both unsupervised and supervised learning to improve upon the performance of traditional classification methods. Semi-supervised clustering, in particular, explicitly integrates both information about the data distribution and about class memberships into the clustering process. In this paper, the potential of a multiobjective formulation of the semi-supervised clustering problem is explored, and two evolutionary multi-objective approaches to the problem are outlined. Experimental results demonstrate practical performance benefits of this methodology, including an improved classification performance and an increased robustness towards annotation errors.

Original languageEnglish
Title of host publicationGECCO 2006 - Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery
Number of pages8
ISBN (Print)1595931864, 9781595931863
Publication statusPublished - 2006
Event8th Annual Genetic and Evolutionary Computation Conference 2006 - Seattle, WA, United States
Duration: 8 Jul 200612 Jul 2006

Publication series

NameGECCO 2006 - Genetic and Evolutionary Computation Conference


Conference8th Annual Genetic and Evolutionary Computation Conference 2006
Country/TerritoryUnited States
CitySeattle, WA


  • Multiobjective clustering
  • Multiobjective machine learning
  • Semi-supervised clustering
  • Semi-supervised learning


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