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Reconnoitering the class distinguishing abilities of the features, to know them better
In this work, we explain the features on the basis of their class or category-distinguishing capabilities.
Random Walk-steered Majority Undersampling
In this work, we propose Random Walk-steered Majority Undersampling (RWMaU), which undersamples the majority points of a class imbalanced dataset, in order to balance the classes.
Integrating Unsupervised Clustering and Label-specific Oversampling to Tackle Imbalanced Multi-label Data
The training dataset is augmented with the set of label-specific synthetic minority points, and classifiers are trained to predict the relevance of each label independently.
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