In the paper Improved Overlap-Based Undersampling for Imbalanced Dataset Classification with Application to Epilepsy and Parkinson’s Disease, the authors introduced two new methods that address the class overlap problem in imbalanced datasets. The methods involve identification and removal of potentially overlapped majority class instances. Extensive evaluations were carried out using 136 datasets and compared against several state-of-the-art methods. Results showed competitive performance with those methods, and statistical tests proved significant improvement in classification results. The discussion on the paper related to the behavioral analysis of class overlap and method validation was raised by Fernández. In this article, the response to the discussion is delivered. Detailed clarification and supporting evidence to answer all the points raised are provided.

中文翻译：

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对“改进的基于重叠的欠采样以用于癫痫和帕金森病的不平衡数据集分类”的讨论的回应，

在论文改进的基于重叠的欠采样用于不平衡数据集分类和应用于癫痫和帕金森病中，作者介绍了两种新方法来解决不平衡数据集中的类重叠问题。这些方法涉及识别和删除可能重叠的多数类实例。使用 136 个数据集进行了广泛的评估，并与几种最先进的方法进行了比较。结果显示这些方法具有竞争力的性能，统计测试证明分类结果有显着改善。Fernández 提出了有关类重叠的行为分析和方法验证的论文的讨论。在本文中，对讨论进行了回应。