A Hybrid Fuzzy-SVM classifier, applied to gene expression profiling for automated leukaemia diagnosis

Meir Perez, David M. Rubin, Lesley E. Scott, Tshilidzi Marwala, Wendy Stevens

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

10 Citations (Scopus)

Abstract

A Hybrid Fuzzy-SVM classifier, used to automate leukaemia diagnosis based on microarray gene expression data, is presented. A publically available dataset was used to develop and test the classifier. A Fuzzy Gene Filter was developed to select the genes which show significant class variation between various leukaemia types. The results obtained from using all the genes for classification is compared to those obtained when only the top 25 differentiating genes are used. The filtered gene classifier was able to correctly classify the entire test dataset, compared to the unfiltered gene classifier which was only able to achieve an accuracy of 84.2%. The results show that, by reducing dimensionality, classification accuracy is improved since redundant information is excluded, thereby limiting the effect of potential outliers.

Original languageEnglish
Title of host publication2008 IEEE 25th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2008
Pages41-45
Number of pages5
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event2008 IEEE 25th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2008 - Eilat, Israel
Duration: 3 Dec 20085 Dec 2008

Publication series

NameIEEE Convention of Electrical and Electronics Engineers in Israel, Proceedings

Conference

Conference2008 IEEE 25th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2008
Country/TerritoryIsrael
CityEilat
Period3/12/085/12/08

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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