Response to discussion on “Improved overlap-based undersampling for imbalanced dataset classification with application to epilepsy and Parkinson’s disease.”
Vuttipittayamongkol, Pattaramon; Elyan, Eyad
Professor Eyad Elyan email@example.com
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
VUTTIPITTAYAMONGKOL, P. and ELYAN, E. 2020. Response to discussion on “Improved overlap-based undersampling for imbalanced dataset classification with application to epilepsy and Parkinson’s disease.”. International journal of neural systems [online], 30(9), article ID 2075002. Available from: https://doi.org/10.1142/s0129065720750027
|Journal Article Type||Letter|
|Acceptance Date||May 25, 2020|
|Online Publication Date||Aug 12, 2020|
|Publication Date||Sep 30, 2020|
|Deposit Date||Oct 19, 2020|
|Publicly Available Date||Aug 13, 2021|
|Journal||International Journal of Neural Systems|
|Publisher||World Scientific Publishing|
|Peer Reviewed||Peer Reviewed|
|Item Discussed||FERNÁNDEZ, A. 2020. Discussion on Vuttipittayamongkol, P. and Elyan, E., Improved overlap-based undersampling for imbalanced dataset classification with application to epilepsy and Parkinson's disease. International journal of neural systems [online], 30(|
|Keywords||Class overlap; Imbalanced data; Undersampling; Classification; Medical; Fuzzy C-means|
|Related Public URLs||https://rgu-repository.worktribe.com/output/940589|
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