Improving robustness of gene ranking by resampling and permutation based score correction and normalization

Feature ranking, which ranks features via their individual importance, is one of the frequently used feature selection techniques. Traditional feature ranking criteria are apt to produce inconsistent ranking results even with light perturbations in training samples when applied to high dimensional and small-sized gene expression data. A widely used strategy for solving the inconsistencies is the multi-criterion combination. But one problem encountered in combining multiple criteria is the score normalization. In this paper, problems in existing methods are first analyzed, and a new gene importance transformation algorithm is then proposed. Experimental studies on three popular gene expression datasets show that the multi-criterion combination based on the proposed score correction and normalization produces gene rankings with improved robustness.