An efficient multi-label classification system using ensemble of classifiers
Shilpa A Chandran, Janu R. Panicker · 2017
Multi-label classification predicts the existence or non existence of labels of an instance which is correlated with multiple class labels. An efficient multi-label classification system uses a pre-clustered training set for learning a group of classifiers arranged as hierarchical tree to predict the labels of a particular example in the test set. The higher levels of the trees contain relevant labels with more distinguishable label ideas and the labels are transferred into the levels that is lower in position of the hierarchical tree that are difficult to distinguish. The admissible labels are then aggregated from the top level to the bottom level of the hierarchical tree to predict the labels of particular example in the test set. The expirimental study points out the improvement of An efficient system in terms of evaluation measures.