For the feature selection problem in pattern classification, based on the criteria of ReliefF, discernability and
correlation respectively, the evaluation and ranking of the features are implemented. All these evaluation criteria for feature
selection are comprehensively used according to rank-level fusion. Experimental results based on several datasets show that
the proposed approach can effectively reduce the feature dimensionality and, at the same time, outcomes better classification
performance compared to the traditional single criterion approach.