Local decomposition for rare class analysis

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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 da...
Junjie Wu, Hui Xiong, Peng Wu, Jian Chen
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2007
Where KDD
Authors Junjie Wu, Hui Xiong, Peng Wu, Jian Chen
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