Fuzzy classification of gene expression data

Gerald Schaefer*, Tomoharu Nakashima, Yasuyuki Yokota, Hisao Ishibuchi

*Corresponding author for this work

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

Abstract

Microarray expression studies measure, through a hybridisation process, the levels of genes expressed in biological samples. Knowledge gained from these studies is deemed increasingly important due to its potential of contributing to the understanding of fundamental questions in biology and clinical medicine. One important aspect of microarray expression analysis is the classification of the recorded samples which poses many challenges due to the vast number of recorded expression levels compared to the relatively small numbers of analysed samples. In this paper we show how fuzzy rulebased classification can be applied successfully to analyse gene expression data. The generated classifier consists of an ensemble of fuzzy if-then rules which together provide a reliable and accurate classification of the underlying data. Experimental results on several standard microarray datasets confirm the efficacy of the approach.

Original languageEnglish
Title of host publicationIEEE International Conference on Fuzzy Systems
PublisherIEEE
ISBN (Print)1424412102, 9781424412105
DOIs
Publication statusPublished - 1 Dec 2007
Event2007 IEEE International Conference on Fuzzy Systems, FUZZY - London, United Kingdom
Duration: 23 Jul 200726 Jul 2007

Conference

Conference2007 IEEE International Conference on Fuzzy Systems, FUZZY
CountryUnited Kingdom
CityLondon
Period23/07/0726/07/07

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    Schaefer, G., Nakashima, T., Yokota, Y., & Ishibuchi, H. (2007). Fuzzy classification of gene expression data. In IEEE International Conference on Fuzzy Systems [4295519] IEEE. https://doi.org/10.1109/FUZZY.2007.4295519