Online Sparse Streaming Feature Selection with Uncertainty.

Online streaming feature selection (OSFS), which conducts feature selection in an online manner, plays an important role in dealing with high-dimensional data. In many real applications such as intelligent healthcare platform, streaming feature always has some missing data, which raises a crucial challenge in conducting OSFS, i.e., how to establish the uncertain relationship between sparse streaming features and labels. Unfortunately, existing OSFS algorithms never consider such uncer-tain relationship. To fill this gap, we in this paper propose an online sparse streaming feature selection with uncertainty (OS ² FSU) algorithm. OS ² FSU consists of two main parts: 1) latent factor analysis is utilized to pre-estimate the missing data in sparse streaming features before conducting feature selection, and 2) fuzzy logic and neighborhood rough set are employed to alleviate the uncertainty between estimated streaming features and labels during conducting feature selection. In the experi-ments, OS ² FSU is compared with five state-of-the-art OSFS algorithms on six real datasets. The results demonstrate that OS ² FSU outperforms its competitors when missing data are en-countered in OSFS.