GA-Based Feature Selection Method for Imbalanced Data with Application in Radio Signal Recognition

This paper presents an improved genetic algorithm (GA) based feature selection method for imbalanced data classification, which is then applied to radio signal recognition of ground-air communication. The proposed method improves the fitness function while SVM is selected as the classifier due to its good classification performance. This method is firstly evaluated using several benchmark datasets and experimental results show that the proposed method outperforms the original GA-based feature selection method now that it not only reduces the feature dimension effectively, but also improves the precision of the minor class. Finally, the proposed method is applied to a real world application in radio signal recognition of ground-air communication, which again shows comparatively better performance.