Online streaming feature selection for multigranularity hierarchical classification learning

Abstract

AbstractHierarchical classification learning is a hot research topic in machine learning and data mining domains, and many feature selection algorithms with category hierarchy have been proposed. However, existing algorithms assume that the feature space of data is completely obtained in advance, and ignore its uncertainty and dynamicity. To address these problems, we propose an online streaming feature selection framework with a hierarchical structure to solve the above two problems simultaneously. First, we apply the hierarchical relationship between nodes in a hierarchical structure to the Relief algorithm, so that it can be used to compute the weights of dynamic features. Second, we dynamically select important features for each internal node via comparing the magnitude of the weights of features on these nodes with their parent and sibling nodes. In addition, we perform redundancy analysis of features by calculating the covariance between features to obtain a superior online feature subset for each internal node. Finally, the proposed algorithm is compared with six online streaming feature selection methods on six hierarchical data sets, and experimental results shows that the classification performance of the proposed algorithm is effective.