基于邻域粗糙集的大规模层次分类在线流特征选择

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

在分类学习任务中,数据的类标记空间存在层次化结构,特征空间伴随着未知性和演化性.因此,文中提出面向大规模层次分类学习的在线流特征选择框架.定义面向层次化结构数据的邻域粗糙模型,基于特征相关性进行重要特征动态选择.最后,基于特征冗余性进行鉴别冗余动态特征.实验验证文中算法的有效性.

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.