| Abstract | The class imbalance problem is a pervasive issue in many real-world domains. Oversampling methods that inflate the rare class by generating synthetic data are amongst the most popular techniques for resolving class imbalance. However, they concentrate on the characteristics of the minority class and use them to guide the oversampling process. By completely overlooking the majority class, they lose a global view on the classification problem and, while alleviating the class imbalance, may negatively impact learnability by generating borderline or overlapping instances. This becomes even more critical when facing extreme class imbalance, where the minority class is strongly underrepresented and on its own does not contain enough information to conduct the oversampling process. We propose a framework for synthetic oversampling that, unlike existing resampling methods, is robust on cases of extreme imbalance. The key feature of the framework is that it uses the density of the well-sampled majority class to guide the generation process. We demonstrate implementations of the framework using the Mahalanobis distance and a radial basis function. We evaluate over 25 benchmark datasets and show that the framework offers a distinct performance improvement over the existing state-of-the-art in oversampling techniques. |
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