One of the significant concerns in radio galaxy image classification is how to deal with highdimensional feature space. Hence, in this paper, we design the segment-based hybrid feature selection scheme (SHFSS) via coupling information theory (filter phase) and machine learning algorithms (wrapper phase). Experimental results show that the selected features bring a higher accuracy than the original dimensions of the dataset for labeling samples.
