Multi Objective Feature Selection

Multi-objective feature selection aims to optimize multiple, often conflicting, criteria simultaneously during feature subset selection, such as maximizing classification accuracy while minimizing the number of features. Current research emphasizes improving the diversity and efficiency of search algorithms, particularly evolutionary algorithms like NSGA-II and its variants, often incorporating techniques to address issues like noisy labels and high dimensionality. This field is crucial for enhancing the performance, interpretability, and efficiency of machine learning models across various applications, including network intrusion detection, healthcare monitoring, and digital pathology.