A heuristic biomarker selection approach based on professional tennis player ranking strategy

Authors:
Bin Han;Ruifei Xie;Lihua Li;Lei Zhu;Shen Wang
Affiliations:
-;-;-;-;-
Venue:
Computer Methods and Programs in Biomedicine
Year:
2014

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Abstract

Extracting significant features from high-dimension and small sample size biological data is a challenging problem. Recently, Michal 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.