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.
|Title of host publication||IEEE International Conference on Fuzzy Systems|
|ISBN (Print)||1424412102, 9781424412105|
|Publication status||Published - 1 Dec 2007|
|Event||2007 IEEE International Conference on Fuzzy Systems, FUZZY - London, United Kingdom|
Duration: 23 Jul 2007 → 26 Jul 2007
|Conference||2007 IEEE International Conference on Fuzzy Systems, FUZZY|
|Period||23/07/07 → 26/07/07|