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PAKDD
2010
ACM

Generating Diverse Ensembles to Counter the Problem of Class Imbalance

10 years 3 months ago
Generating Diverse Ensembles to Counter the Problem of Class Imbalance
Abstract. One of the more challenging problems faced by the data mining community is that of imbalanced datasets. In imbalanced datasets one class (sometimes severely) outnumbers the other class, causing correct, and useful predictions to be difficult to achieve. In order to combat this, many techniques have been proposed, especially centered around sampling methods. In this paper we propose an ensemble framework that combines random subspaces with sampling to overcome the class imbalance problem. We then experimentally verify this technique on a wide variety of datasets. We conclude by analyzing the performance of the ensembles, and showing that, overall, our technique provides a significant improvement.
T. Ryan Hoens, Nitesh V. Chawla
Added 14 Oct 2010
Updated 14 Oct 2010
Type Conference
Year 2010
Where PAKDD
Authors T. Ryan Hoens, Nitesh V. Chawla
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