A Multilevel Optimal Feature Selection and Ensemble Learning for a Specific CAD System-Pulmonary Nodule Detection

The traditional motivation behind feature selection algorithms is to find the best subset of features for a task using one particular learning algorithm. However, it has been often found that no single classifier is entirely satisfactory for a particular task. Therefore, how to further improve the performance of these single systems on the basis of the previous optimal feature subset is a very important issue.We investigate the notion of optimal feature selection and present a practical feature selection approach that is based on an optimal feature subset of a single CAD system, which is referred to as a multilevel optimal feature selection method (MOFS) in this paper. Through MOFS, we select the different optimal feature subsets in order to eliminate features that are redundant or irrelevant and obtain optimal features.