title: A heuristic biomarker selection approach based on professional tennis player ranking strategy.
abstract: Extracting significant features from high-dimension and small sample size biological data is a challenging problem. Recently, Michał Draminski proposed the Monte Carlo feature selection (MC) algorithm, which was able to search over large feature spaces and achieved better classification accuracies. However in MC the information of feature rank variations is not utilized and the ranks of features are not dynamically updated. Here, we propose a novel feature selection algorithm which integrates the ideas of the professional tennis players ranking, such as seed players and dynamic ranking, into Monte Carlo simulation. Seed players make the feature selection game more competitive and selective. The strategy of dynamic ranking ensures that it is always the current best players to take part in each competition. The proposed algorithm is tested on 8 biological datasets. Results demonstrate that the proposed method is computationally efficient, stable and has favorable performance in classification.
authors.affiliation: College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou, People's Republic of China. Electronic address: hanb.bin@gmail.com.
authors.forename: Bin
authors.initials: B
authors.lastname: Han
authors.forename: Ruifei
authors.initials: R
authors.lastname: Xie
authors.forename: Lihua
authors.initials: L
authors.lastname: Li
authors.forename: Lei
authors.initials: L
authors.lastname: Zhu
authors.forename: Shen
authors.initials: S
authors.lastname: Wang
mesh_terms: D015415:Biomarkers; D006801:Humans; D009010:Monte Carlo Method; D013715:Tennis
chemical_list: D015415:Biomarkers
keywords: Dynamic ranking; Feature selection; Microarray; Monte Carlo