A Hybrid Resampling Approach for Multiclass Skewed Datasets and Experimental Analysis with Diverse Classifier Models

Main Article Content

Rose Mary Mathew, R. Gunasundari

Abstract

In real-life scenarios, imbalanced datasets pose a prevalent challenge for classification tasks, where certain classes are heavily underrepresented compared to others. To combat this issue, this article introduces DOSAKU, a novel hybrid resampling technique that combines the strengths of DOSMOTE and AKCUS algorithms. By integrating both oversampling and undersampling methods, DOSAKU significantly reduces the imbalance ratio of datasets, enhancing the performance of classifiers. The proposed approach is evaluated on multiple models employing different classifiers, and the results demonstrate its superiority over existing resampling measures, making it an effective solution for handling class imbalance challenges. DOSAKU's promising performance is a substantial contribution to the field of imbalanced data classification, as it offers a robust and innovative solution for improving predictive model accuracy and fairness in real-world applications where imbalanced datasets are common.

Article Details

How to Cite
Rose Mary Mathew, et al. (2023). A Hybrid Resampling Approach for Multiclass Skewed Datasets and Experimental Analysis with Diverse Classifier Models. International Journal on Recent and Innovation Trends in Computing and Communication, 11(10), 1108–1114. https://doi.org/10.17762/ijritcc.v11i10.8631
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Articles
Author Biography

Rose Mary Mathew, R. Gunasundari

Rose Mary Mathew1, Dr. R. Gunasundari2

1Research Scholar, Department of Computer Science,

Karpagam Academy of Higher Education ,

Coimbatore, India

rosem.mathew@gmail.com

https://orcid.org/0000-0003-0555-4873

2Professor, Department of Computer Applications,

Karpagam Academy of Higher Education,

Coimbatore, India

gunasoundar04@gmail.com

https://orcid.org/0000-0003-4157-285X