The algorithm should be computationally cheap as well as accurate, so it fits well with active learning and other problems that require frequent iterations on large datasets.
we propose a novel non-linear distribution spreading algorithm, which first uses Delta IDF technique (Martineau and Finin, 2009) to weight features, and then leverages the distribution of Delta IDF scores of a feature across different classes to efficiently recognize discriminative features for the classification task in the presence of mislabeled data.