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Response to Discussion on “Improved Overlap-Based Undersampling for Imbalanced Dataset Classification with Application to Epilepsy and Parkinson’s Disease,” by:0 (Source: Crossref)

    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.

    International Journal of Neural Systems, 30:8, August 2020


    • 1. P. Vuttipittayamongkol, E. Elyan, A. Petrovski and C. Jayne , Overlap-based undersampling for improving imbalanced data classification, Int. Conf. Intell. Data Eng. Autom. Learn. (Springer, 2018), pp. 689–697. CrossrefGoogle Scholar
    • 2. J. Arora, K. Khatter and M. Tushir , Fuzzy c-means clustering strategies: A review of distance measures, in Software Engineering (Springer, 2019), pp. 153–162. CrossrefGoogle Scholar
    • 3. B. R. Walker and N. R. Colledge , Davidson’s Principles and Practice of Medicine, 23rd edn. (Elsevier Health Sciences, 2013). Google Scholar
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