Professional tennis player ranking strategy based Monte Carlo feature selection

Extracting significant features from high-dimensional and small sample-size microarray data is a challenging problem. Other than wrapper or filter methods, we propose a novel feature selection algorithm which integrates the ideas of professional tennis players ranking, such as seed players and dynamic ranking with Monte Carlo simulation. Seed players make the ‘game’ more competitive and selective, hence improve the selection efficiency. Besides, the ranks of features are dynamically updated and this ensures that it is always the current best players to take part in each competitions. The proposed algorithm is tested on widely used public datasets. Results demonstrate that the proposed method comparatively converges faster, more stable and has good performance in classification and therefore is an efficient algorithm for feature selection.