Multi-objective evolutionary algorithms based interpretable fuzzy models for microarray gene expression data analysis

Zhenyu Wang, Vasile Palade

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

2 Citations (Scopus)

Abstract

We believe the great interpretability of fuzzy models allow fuzzy-based methods to play a very important role in Microarray gene expression data analysis, but the advantages offered by fuzzy-based techniques in this application have not yet been fully explored in the literature. In this paper, we construct Multi-Objective Evolutionary Algorithms based Interpretable Fuzzy (MOEAIF) models for microarray gene expression data analysis. Our novel fuzzy models can significantly decrease the model complexity, and automatically balance the accuracy and interpretability of the models. The experimental studies have shown that relatively simple and small fuzzy rule bases, with satisfactory classification performance, have been successful found for challenging microarray gene expression datasets. Cancer gene expression data, fuzzy rule-based.

Original languageEnglish
Title of host publicationProceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010
PublisherIEEE
Pages308-313
Number of pages6
ISBN (Print)9781424483075
Publication statusPublished - 2010
Externally publishedYes
Event2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010 - Hong Kong, China
Duration: 18 Dec 201021 Dec 2010

Conference

Conference2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010
CountryChina
CityHong Kong
Period18/12/1021/12/10

Keywords

  • Cancer gene expression data
  • Evolutionary algorithms
  • Feature selection
  • Fuzzy rule-based systems
  • Microarray data analysis
  • Model interpretability
  • Multi-objective optimisation

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

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  • Cite this

    Wang, Z., & Palade, V. (2010). Multi-objective evolutionary algorithms based interpretable fuzzy models for microarray gene expression data analysis. In Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010 (pp. 308-313). [5706582] IEEE.