Penalty-based cluster validity index for class discovery from cancer data

Zhiwen Yu, Jia You, Guihua Wen

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

Abstract

In order to perform successful diagnosis and treatment of cancer, discovering and classifying cancer types correctly is essential. One of the challenges in cancer class discovery is to estimate the number of classes given a set of unknown microarray data. In the paper, we propose a new cluster validity criterion called Penalty-based Disagreement Index (PDI) based on the perturbation technique to estimate the number of classes in microarray data, PDI not only considers the disagreement between the partition results obtained from the original data and those obtained from the perturbed data, but also includes a penalty measure which is a function of the number of classes. Our experiments show that PDI successfully estimates the true number of classes in a number of challenging real cancer datasets.
Original languageEnglish
Title of host publicationProceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
Pages1577-1582
Number of pages6
Volume4
DOIs
Publication statusPublished - 7 Nov 2011
Event2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011 - Guilin, Guangxi, China
Duration: 10 Jul 201113 Jul 2011

Conference

Conference2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
CountryChina
CityGuilin, Guangxi
Period10/07/1113/07/11

Keywords

  • Cancer data
  • Class discovery
  • Cluster ensemble

ASJC Scopus subject areas

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

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