School of Computing, Robert Gordon University, Garthdee Road, Aberdeen, AB10 7GJ, UK.
Int J Neural Syst. 2020 Sep;30(9):2075002. doi: 10.1142/S0129065720750027. Epub 2020 Aug 12.
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.
在《基于改进重叠的不平衡数据集分类抽样方法及其在癫痫和帕金森病中的应用》一文中，作者介绍了两种新的方法，用于解决不平衡数据集中的类重叠问题。这些方法涉及到识别和删除潜在重叠的多数类实例。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。结果表明，这些方法具有竞争力，并且统计测试证明了分类结果的显著改善。Fernández 提出了与类重叠行为分析和方法验证有关的讨论。本文给出了对该讨论的回应。提供了详细的澄清和支持证据来回答提出的所有观点。
