BDT: A Novel Approach to Handle Imbalanced Data in Machine Learning Models
Keywords:
Data Imbalance, Machine Learning, Under-Sampling, Over-Sampling, Model Performance, Algorithm Adjustment, Imbalanced Data Correction TechniqueAbstract
In the realm of machine learning and data science, the issue of imbalanced datasets presents a significant challenge, often leading to biased models and inaccurate predictions. This research introduces a novel technique aimed at mitigating the effects of data imbalance, thereby enhancing model performance across various metrics. Through a rigorous examination of existing imbalance correction methods, this study identifies key gaps and proposes an innovative approach: Balanced Data Technique (BDT) that combines under-sampling, over-sampling, and algorithmic adjustment methods in a unique framework. Employing a comprehensive experimental setup across multiple imbalanced datasets, the technique demonstrates superior performance in comparison to established methods, as evidenced by improved accuracy, precision, and recall scores. This paper details the development process of the technique, from theoretical underpinnings through to practical implementation and testing. The implications of this research are far-reaching, offering potential improvements in fields where imbalanced data is prevalent. By addressing this fundamental issue, the proposed technique contributes to the advancement of more equitable and effective machine learning models.
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