Title: Mining classification rules for gene expression data using enhanced quickreduct fuzzy-rough feature selection and ant colony optimisation
Authors: Chinnanambalam Ponniah Chandran; Gurusamy Arumugam
Addresses: Department of Computer Science, Ayya Nadar Janaki Ammal College, Sivakasi – 626 124, India. ' Department of Computer Science, Madurai Kamaraj University, Madurai – 625 021, India
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.
Keywords: classification rules; feature selection; gene expression data; yeast S.Cerevisiae dataset; quickreduct; fuzzy logic; rough sets; ant colony optimisation; ACO; data mining; feature extraction.
International Journal of Granular Computing, Rough Sets and Intelligent Systems, 2012 Vol.2 No.3, pp.179 - 195
Available online: 24 May 2012 *Full-text access for editors Access for subscribers Purchase this article Comment on this article