Linear Classifier

Linear classifiers are fundamental machine learning models aiming to separate data points into different classes using a linear decision boundary. Current research focuses on improving their performance in challenging scenarios, such as imbalanced datasets (through techniques like AUC maximization) and high-dimensional spaces (using optimized algorithms and feature extraction methods), as well as exploring their integration within larger architectures like multi-center classifiers and ensembles. The widespread use of linear classifiers stems from their computational efficiency and interpretability, making them valuable baselines and components in various applications, from medical image analysis to natural language processing.