A general framework for multi-label learning towards class correlations and class imbalance

In multi-label classification settings, one of the most common problems is the massive label output space. To alleviate this, some methods opt to exploit label correlations to reduce the output space during prediction. However, these methods sacrifice efficiency or ignore global label correlations. In addition, label imbalances are another problem that is prevalent in multi-label classification. Current methods of correcting for imbalance oftentimes use single-label methods, which fail to consider label correlations. In this paper, we introduce general frameworks that incorporate topic modeling to seamlessly address both problems. We show that these frameworks can allow even the most naïve methods, such as Binary Relevance, to perform similarly to state-of-the-art methods. Furthermore, we show that our frameworks can also adapt state-of-the-art methods to perform better than the methods by themselves.