Mining classification rules for gene expression data using enhanced quickreduct fuzzy-rough feature selection and ant colony optimisation Online publication date: Fri, 29-Aug-2014
by Chinnanambalam Ponniah Chandran; Gurusamy Arumugam
International Journal of Granular Computing, Rough Sets and Intelligent Systems (IJGCRSIS), Vol. 2, No. 3, 2012
Abstract: In this paper, the mining classification rules for gene expression data of yeast S.Cerevisiae is carried out using enhanced quickreduct fuzzy-rough feature selection (EQRFR-FS) with ant colony optimisation (ACO). Rough sets theory deals with uncertainty and vagueness of an information system in data mining. The ACO algorithms have been applied to combinatorial optimisation problems and data mining classification problems. The analyses are carried out in two phases. In the first phase, the features are extracted from the gene expression data and then feature selection is carried out from the extracted features using EQRFR-FS algorithm. In the second phase, from reduct set the classification rules mining are carried out with ant-miner algorithm. The dataset is obtained from the expression profiles maintained by Brown's group at Stanford University.
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