An Oversampling Mechanism for Multimajority Datasets using SMOTE and Darwinian Particle Swarm Optimisation

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Describing the various stages in the experimental study
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Published: Mar 10, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i2.6139
Keywords:
imbalanced, optimization, oversampling, multiclass, multimajority

Main Article Content

Rose Mary Mathew
Research Scholar, Department of Computer Science, Karpagam Academy of Higher Education , Coimbatore, India
R. Gunasundari
Professor, Department of Computer Applications, Karpagam Academy of Higher Education, Coimbatore, India

Abstract

Data skewness continues to be one of the leading factors which adversely impacts the machine learning algorithms performance. An approach to reduce this negative effect of the data variance is to pre-process the former dataset with data level resampling strategies. Resampling strategies have been seen in two forms, oversampling and undersampling. An oversampling strategy is proposed in this article for tackling multiclass imbalanced datasets. This proposed approach optimises the state-of-the-art oversampling technique SMOTE with the Darwinian Particle Swarm Optimization technique. This proposed method DOSMOTE generates synthetic optimised samples for balancing the datasets. This strategy will be more effective on multimajority datasets.  An experimental study is performed on peculiar multimajority datasets to measure the effectiveness of the proposed approach. As a result, the proposed method produces promising results when compared to the conventional oversampling strategies.

Article Details

How to Cite
Mary Mathew, R. ., & Gunasundari, R. . (2023). An Oversampling Mechanism for Multimajority Datasets using SMOTE and Darwinian Particle Swarm Optimisation. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2), 143–153. https://doi.org/10.17762/ijritcc.v11i2.6139
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Vol. 11 No. 2 (2023): February (2023) Issue
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