Following this idea, we develop computationally cheap feature weighting techniques to counteract such effect by boosting the weight of discriminative features, so that they would not be subdued and the instances with such features would have higher chance to be correctly classified.
we propose a non-linear distribution spreading algorithm for feature weighting.