Imbalanced data classification is a prevalent challenge in real-world applications. While a conventional sphere-based classification algorithm, random sphere cover (RSC), evenly constructs a set of spheres for two classes in balanced data using a parameter for the minimum sphere size, it struggles with constructing minority spheres in class-imbalanced data. Although RSC can be combined with existing oversampling methods, this approach requires additional hyperparameters, and its effectiveness decreases as the minority size decreases. To overcome these issues, we propose a novel approach that employs the area under the receiver operating characteristic curve (AUC) to construct and expand spheres for minority class. This parameter-free sphere classifier considers both the majority and minority classes simultaneously. We conducted a thorough experiment on both synthetic and 50 real datasets, which revealed that our proposed method outperformed existing various oversampling techniques with the lowest training time.

中文翻译：

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PFSC：用于不平衡数据分类的无参数球分类器

不平衡的数据分类是现实应用中普遍存在的挑战。虽然传统的基于球体的分类算法随机球体覆盖 (RSC) 使用最小球体大小的参数均匀地为平衡数据中的两个类别构造一组球体，但它很难在类不平衡数据中构造少数球体。尽管 RSC 可以与现有的过采样方法相结合，但这种方法需要额外的超参数，并且其有效性随着少数规模的减小而降低。为了克服这些问题，我们提出了一种新方法，利用接收者操作特征曲线（AUC）下的面积来构建和扩展少数群体的范围。这种无参数球分类器同时考虑多数类和少数类。我们对合成数据集和 50 个真实数据集进行了彻底的实验，结果表明我们提出的方法以最短的训练时间优于现有的各种过采样技术。