Optimizing Binary Classification: An Alternative New Loss Function with Statistical Validation

Binary classification is a fundamental task in machine learning with applications in financial fraud detection, robotics, sentiment analysis, health care, and autonomous systems. The binary cross-entropy loss function is more widely used for optimizing classification models. However, sometimes it demonstrates numerical instability and high variance during the training leading to inconsistent convergence. To solve this issue, We propose an alternative loss function designed for binary classification to improve numerical stability and reduce training fluctuations. Experimental results show that the proposed loss function achieves smoother training dynamics and lower variance in loss curves compared to conventional approaches. These findings suggest that it serves as a robust alternative for binary classification tasks, improving model reliability in real-world applications.