Binary Classification

Binary classification, the task of assigning data points to one of two categories, is a fundamental problem in machine learning with applications across diverse fields. Current research emphasizes addressing challenges like class imbalance through techniques such as synthetic data generation, loss function modification, and decision threshold calibration, often employing models like Support Vector Machines, deep neural networks (including transformers like BERT and RoBERTa), and ensemble methods. These advancements aim to improve classification accuracy, fairness, and efficiency, particularly in scenarios with limited data or noisy labels, impacting fields ranging from medical diagnosis to fraud detection. Furthermore, ongoing work focuses on developing robust evaluation metrics and optimizing model architectures for specific data characteristics and application needs.