Large-Scale Hierarchical Classification Online Streaming Feature Selection Based on Neighborhood Rough Set

Label space of data possesses a hierarchical structure, and feature space is unknown and evolutionary in many classification learning tasks. An online streaming feature selection framework for large-scale hierarchical classification task is proposed. Firstly, a neighborhood rough model is defined for hierarchical structure data. Important features are dynamically selected based on feature correlation. Finally, the redundant dynamic features are identified based on feature redundancy. Experiments are conducted to verify the effectiveness of the proposed algorithm. © 2019, Science Press. All right reserved.