Recursion-and-Fuzziness Reinforced Online Sparse Streaming Feature Selection

Online streaming feature selection (OSFS) is a critical technique for addressing high-dimensional streaming data in various real applications. The challenge of OSFS arises from missing entries due to various reasons such as equipment failure or human mistakes. Online sparse streaming feature selection (OS$^{2}$FS) is a feasible approach to this challenge by pre-estimating the missing data before feature selection based on latent factor analysis (LFA). However, such an approach separates the processes of the LFA-based estimates and the down-streaming feature selection, which cannot represent the uncertain relationships between the sparse features and the labels, thereby leading to accuracy losses. To address this critical issue, this article proposes a recursion-and-fuzziness reinforced online sparse streaming feature selection (RF-OS$^{2}$FS) model, which consists of two-fold ideas: first, connecting the LFA-based feature estimation process and the consequent feature selection process via a recursively completion sampling strategy, therefore enabling the information feedback loop from the feature selection validation results to the missing feature estimation, and second, adopting the three-way decisions strategy to establish the fuzzy feature selection for representing the uncertain relationships between the sparse features and the labels. Experimental results on ten real-world benchmark datasets demonstrate that the proposed RF-OS$^{2}$FS model significantly out-performs existing state-of-the-arts, both OSFS and OS$^{2}$FS models in terms of accuracy when performing sparse streaming feature selection for down-streaming classification tasks.