Independent Classifier

Independent classifiers are machine learning models that make predictions without relying on correlations between input features, addressing limitations of methods that assume feature independence (like Naive Bayes) or suffer from spurious correlations. Current research focuses on improving their accuracy and efficiency, exploring techniques like adaptive sample selection for heterogeneous datasets and novel feature partitioning methods to mitigate biases. These advancements are crucial for enhancing the reliability and interpretability of machine learning models across diverse applications, particularly in areas like medical image analysis and natural language processing where feature independence is often violated.