Abstract
In the supplied ASD dataset, it is typically seen that there is an extremely large imbalance in the number of samples for two classes, leading to an imbalance. Without addressing this issue, applying binary classification algorithms to such data would produce an extremely biased result. It affects the relationships between features as well. These misclassifications could affect the decision regarding medical treatment and result in a protracted delay for those who urgently require medical intervention. In the current study, we use a variety of resampling strategies to address the issue of class imbalance. Precision, Recall, and F1-score are used as evaluation measures for all models. We have also looked at AUC score, which demonstrates encouraging outcomes for the use of resampling methods for imbalanced dataset.
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The work carried out by the first author is supported by the GATE scholarship from Ministry of Education, India.
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Gupta, R.K., Dutta, K. (2023). Resampling Strategies for Mitigating Class Imbalance of ASD Dataset on the Performance of Machine Learning Classifiers. In: Borah, S., Gandhi, T.K., Piuri, V. (eds) Advanced Computational and Communication Paradigms . ICACCP 2023. Lecture Notes in Networks and Systems, vol 535. Springer, Singapore. https://doi.org/10.1007/978-981-99-4284-8_18
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