OFHR: Online Streaming Feature Selection With Hierarchical Structure Based on Relief

Abstract

Hierarchical classification learning, an emerging classification task in machine learning, is an essential topic. In which various feature selection algorithms have been proposed to select informative features for hierarchical classification. How-ever, existing hierarchical feature selection algorithms consider that the feature space of data is completely obtained in advance, and neglect the uncertainty and dynamism, i.e., feature arrives dynamically in an online manner. In this paper, we present an online streaming feature selection framework with hierarchical structure. First, we apply the closeness matrix between internal nodes to the Relief algorithm, which can calculate the weights of the dynamic features. Second, significant features are dynamically selected for each internal node by considering the hierarchical relationships and feature weights between nodes in the tree structure. Moreover, we perform redundant analysis of features by calculating the covariance between features, and then 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. The experimental results prove that our algorithm can improve the classification accuracy of the classifier by 10% compared to the suboptimal algorithms, which indicates that the algorithm outperforms other comparative algorithms in hierarchical data sets.