Balanced Dataset

Balanced datasets are crucial for training unbiased machine learning models, particularly in applications where class imbalances can lead to unfair or inaccurate predictions. Current research focuses on mitigating biases stemming from both imbalanced data and inherent model limitations, employing techniques like data augmentation (e.g., using GANs or oversampling methods), architecture optimization (e.g., through neural architecture search), and re-training model layers on balanced subsets. This work is significant because it addresses pervasive issues of fairness and accuracy in machine learning, impacting diverse fields from facial recognition and medical diagnosis to e-commerce and anomaly detection.