title: An effective hybrid approach of gene selection and classification for microarray data based on clustering and particle swarm optimization.
abstract: In this paper, a hybrid approach based on clustering and Particle Swarm Optimisation (PSO) is proposed to perform gene selection and classification for microarray data. In the new method, firstly, genes are partitioned into a predetermined number of clusters by K-means method. Since the genes in each cluster have much redundancy, Max-Relevance Min-Redundancy (mRMR) strategy is used to reduce redundancy of the clustered genes. Then, PSO is used to perform further gene selection from the remaining clustered genes. Because of its better generalisation performance with much faster convergence rate than other learning algorithms for neural networks, Extreme Learning Machine (ELM) is chosen to evaluate candidate gene subsets selected by PSO and perform samples classification in this study. The proposed method selects less redundant genes as well as increases prediction accuracy and its efficiency and effectiveness are verified by extensive comparisons with other classical methods on three open microarray data.
authors.forename: Fei
authors.initials: F
authors.lastname: Han
authors.forename: Shanxiu
authors.initials: S
authors.lastname: Yang
authors.forename: Jian
authors.initials: J
authors.lastname: Guan
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000378:metabolism*; D057225:Data Mining; D030562:Databases, Protein; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009363:Neoplasm Proteins / Q000378:metabolism*; D009369:Neoplasms / Q000175:diagnosis / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins