论文标题
基于伽玛的基于分布的抽样，以实现不平衡数据
Gamma distribution-based sampling for imbalanced data
论文作者
论文摘要
在许多领域中，包括医学诊断，欺诈检测等许多领域中的班级分布是一个常见的问题。它导致分类算法的偏见，导致少数族类数据的性能差。在本文中，我们提出了一种新颖的方法，可以通过智能重新采样少数群体实例来平衡数据中的班级分布。所提出的方法是基于通过伽马分布在现有少数群体附近产生新的少数族裔实例。我们的方法提供了一种自然而连贯的方法来平衡数据。我们对新采样技术进行了全面的数值分析。实验结果表明，所提出的方法的表现优于现有的不平衡数据最新方法。具体而言，新的抽样技术在24个现实生活中的12个以及合成数据集中产生了最佳结果。为了进行比较，SMOTE方法仅在1个数据集上获得最高分数。我们得出的结论是，新技术提供了一种简单而有效的抽样方法来平衡数据。
Imbalanced class distribution is a common problem in a number of fields including medical diagnostics, fraud detection, and others. It causes bias in classification algorithms leading to poor performance on the minority class data. In this paper, we propose a novel method for balancing the class distribution in data through intelligent resampling of the minority class instances. The proposed method is based on generating new minority instances in the neighborhood of the existing minority points via a gamma distribution. Our method offers a natural and coherent approach to balancing the data. We conduct a comprehensive numerical analysis of the new sampling technique. The experimental results show that the proposed method outperforms the existing state-of-the-art methods for imbalanced data. Concretely, the new sampling technique produces the best results on 12 out of 24 real life as well as synthetic datasets. For comparison, the SMOTE method achieves the top score on only 1 dataset. We conclude that the new technique offers a simple yet effective sampling approach to balance data.
