Online streaming feature selection based on hierarchical structure information

Summary

Hierarchical classification learning aims to exploit the hierarchical relationship between data categories. The high dimensionality and dynamic of the data feature space are the main challenges of this research. Hierarchical feature selection uses a hierarchical structure to divide large‐scale tasks into multiple small tasks, which can more effectively improve the training speed and prediction accuracy of classification models. To present, existing online feature selection methods ignore the hierarchical structure of data. In addition, the dependency relationships in the hierarchical structure can serve as auxiliary knowledge to aid feature selection. Based on this, this paper proposes an online streaming hierarchical feature selection method based on kernelized fuzzy rough sets (OFS‐HNFRS). First, we use the prior knowledge of the hierarchical structure to divide the sample set into multiple subsets. Second, the dependency relationship of hierarchical structure is extended to kernelized fuzzy rough sets, and hierarchical category dependency based on kernelized fuzzy rough sets is defined. Finally, a new online feature selection framework is proposed, which is used to evaluate the relevance, significance, and redundancy of features. We verify the effectiveness of the proposed algorithm on six hierarchical datasets and eight flat datasets.