  In this paper, a novel feature selection method is presented, which is based
on Class-Separability (CS) strategy and Data Envelopment Analysis (DEA). To
better capture the relationship between features and the class, class labels
are separated into individual variables and relevance and redundancy are
explicitly handled on each class label. Super-efficiency DEA is employed to
evaluate and rank features via their conditional dependence scores on all class
labels, and the feature with maximum super-efficiency score is then added in
the conditioning set for conditional dependence estimation in the next
iteration, in such a way as to iteratively select features and get the final
selected features. Eventually, experiments are conducted to evaluate the
effectiveness of proposed method comparing with four state-of-the-art methods
from the viewpoint of classification accuracy. Empirical results verify the
feasibility and the superiority of proposed feature selection method.
