PSEFminer: A new probabilistic subspace ensemble framework for cancer microarray data analysis

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. Most of the existing works adopt single clustering algorithms to perform class discovery from bio-molecular data. Unfortunately, single clustering algorithms have limitations, which are lack of the robustness, stableness and accuracy. In this paper, we develop a new probabilistic subspace ensemble framework known as PSEFminer for cancer microarray data analysis. PSEFminer integrates the probabilistic subspace generator, the self-organizing map(SOM) and the normalized cut algorithm into the ensemble framework to discover the underlying structure from cancer microarray data. The experiments in cancer datasets show that (i) the probabilistic subspace generator plays an important role to improve the performance of PSEFminer; (ii) PSEFminer outperforms most of the state-of-the-art cluster ensemble algorithms when applied to cancer gene expression data
Original languageEnglish
Title of host publicationProceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
Pages21-26
Number of pages6
Volume1
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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