Hierarchical Streaming Feature Selection Based on FDAF-score

Abstract

In many practical classification scenarios, the data label space has a hierarchical structure, these data often have high-dimensional features, and there are irrelevant and redundant features. Thus, a variety of hierarchical classification feature selection methods are proposed. However, most of the existing algorithms ignore the temporal uncertainty and unknown of the feature space, and can only deal with static feature space data. Therefore, a new hierarchical streaming feature selection method is proposed. First, the algorithm uses the sibling strategy and the exclusion strategy in the hierarchical relationship to consider the relationship between homogeneous and heterogeneous samples. Then, the FDAF-score algorithm is integrated into the hierarchical classification streaming feature selection and used as a criterion for evaluating feature importance. Second, the improved Gaussian kernel function is used to judge the degree of redundancy between features, and the redundant features are removed. Finally, experiments on six hierarchical classification data sets show the effectiveness of the proposed algorithm.