document: Local decomposition for rare class analysis Given its importance , the problem of predicting rare classes in large-scale multi-labeled data sets has attracted great attentions in the literature . However , the rare-class problem remains a critical challenge , because there is no natural way developed for handling imbalanced class distributions . This paper thus fills this crucial void by developing a method for Classification using lOcal clusterinG ( COG ) . Specifically , for a data set with an imbalanced class distribution , we perform clustering within each large class and produce sub-classes with relatively balanced sizes . Then , we apply traditional supervised learning algorithms , such as Support Vector Machines ( SVMs ) , for classification . Indeed , our experimental results on various real-world data sets show that our method produces significantly higher prediction accuracies on rare classes than state-of-the-art methods . Furthermore , we show that COG can also improve the performance of traditional supervised learning algorithms on data sets with balanced class distributions .
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