title: Feature selection for genomic data sets through feature clustering.
abstract: A subset selected by a supervised feature selection method may not be a good one for unsupervised learning and vice versa. We propose a novel Feature Selection algorithm through Feature Clustering, FSFC. FSFC does not need the class label information in the data set and is suitable for both supervised learning and unsupervised learning. We test FSFC on some biological data sets for both clustering and classification analysis and the results indicates that FSFC algorithm can significantly reduce the original data sets without scarifying the quality of clustering and classification.
authors.affiliation: College of Computer and Information Engineering, Henan University, Kaifeng, Henan 475004, China. zhengfb@henu.edu.cn
authors.forename: Fengbin
authors.initials: F
authors.lastname: Zheng
authors.forename: Xiajiong
authors.initials: X
authors.lastname: Shen
authors.forename: Zhengye
authors.initials: Z
authors.lastname: Fu
authors.forename: Shanshan
authors.initials: S
authors.lastname: Zheng
authors.forename: Guangrong
authors.initials: G
authors.lastname: Li
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D016678:Genome; D023281:Genomics / Q000379:methods*