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Class imbalance occurs when certain categories in a dataset have significantly fewer examples than others. This is common in fraud detection, medical diagnostics, and customer churn prediction. Traditional machine learning models often favor majority classes, resulting in poor performance on minority classes, which can be the most critical for real-world decisions. Organizations need a solution that balances data effectively while preserving its natural distribution.
Rubixe AI Startup Incubation Chamber developed a synthetic data generation algorithm that strategically augments minority classes. Our approach ensures the added data points reflect the real characteristics of the class, improving dataset balance for AI Model Training. This makes models more accurate, fair, and robust.
The algorithm identifies minority classes and analyzes their statistical distribution. Using interpolation and probabilistic sampling, it generates synthetic data points. Each point is validated to ensure it fits naturally within the dataset. The augmented dataset is then used for AI Data Augmentation during model training, helping machine learning models generalize better and improve performance on underrepresented classes.
This approach helps organizations build fairer, more reliable AI models. By addressing class imbalance, our solution ensures critical patterns are not missed, enabling more accurate predictions and actionable insights across industries.
