Four crucial issues are considered by the proposed HoAL: 1) unlike binary cases, the selection granularity for multilabel active learning need to be fined from example to examplelabel pair 2) different labels are seldom independent, and label correlations provide critical information for efficient learning 3) in addition to pair-wise label correlations, high-order label correlations are also informative for multilabel active learning and 4) since the number of label combinations increases exponentially with respect to the number of labels, an efficient mining

method is required to discover informative label correlations

-Four crucial issues are considered by the proposed HoAL: 1) unlike binary cases, the selection granularity for multilabel active learning need to be fined from example to examplelabel pair　2) different labels are seldom independent, and label correlations provide critical information for efficient learning　3) in addition to pair-wise label correlations, high-order label correlations are also informative for multilabel active learning　and 4) since the number of label combinations increases exponentially with respect to the number of labels, an efficient mining

method is required to discover informative label correlations