Bacterial foraging optimization algorithm based feature selection for microarray data classification

Abstract An effective method is needed to extract knowledge and useful information from microarray gene expression datasets. Datasets of microarray gene expression typically consist of large numbers of genes and less samples, making it very difficult to extract data and trends present in the data. To choose the genes with maximum variations that are the most informative and important genes, gene selection is therefore carried out. The genes whose value in the samples does not differ much and remain roughly the same are excluded and are not used for the classifier design. Recent research has focused on the use of data mining to classify microarray data and to identify genes that are differentially regulated by different diseases. In this paper, gene selection was based on the technique of bacterial foraging optimization (BFO). E.coli bacteria develop at a very rapid rate due to the suitable conditions and adequate food, founded on the drinking performance of the Bacteria E.coli. The bacteria are moving into nutrient areas very rapidly and seeking to escape harmful substances. The bacterial movements are called taxes.