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Statistic deviation mode balancer (SDMB): A novel sampling algorithm for imbalanced data
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DOI：10.1016/j.neucom.2025.129484.png)
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Abstract
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In supervised learning, the efficacy of classifier algorithms is heavily dependent on the quality of data. Imbalanced datasets, where the class distribution is not uniform, pose a significant challenge, often leading to suboptimal classifier performance. Traditional approaches to rectifying this imbalance have relied on duplicating minority class instances or generating synthetic data, which can introduce bias or outliers. Our novel Statistic Deviation Mode Balancer (SDMB) algorithm addresses these issues by generating new instances that closely mirror the original data structure. Utilizing standard deviation and mode analysis, SDMB strategically synthesizes minority class data while avoiding the pitfalls of outlier generation. The result is a balanced dataset that facilitates more accurate learning by classifier algorithms. We have rigorously tested SDMB across various datasets and compared its performance against existing balancing methods. Our findings indicate that SDMB not only outperforms its counterparts but also significantly enhances the practical application of classifier algorithms in real-world datasets.
Keywords:
Imbalanced Datasets
Classifier Performance
Data Sampling Techniques
Algorithmic Classification
Diagnostic Analytics
Data Balancing
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