, need to improve the methods based on LabelRank so that it can achieve its full potential

, we plan: (i) to consider more clever partition strategies to make the training more efficient; (ii) to rely on a non-uniform choice from the KNN classifier; (iii) to explore other classifiers; and (iv) to test other voting schemas, As future work

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