부산대학교 도서관

Online streaming feature selection (OSFS) is an effective approach to addressing high-dimensional data. In real big data-related applications, streaming features commonly have massive missing data due to various uncertain factors. The missing data may cause some uncertain relationships between sparse features and labels. However, existing OSFS methods tend to select sparse streaming features based on certain relevance and redundancy analysis, which may erroneously discard some weak relevant but irredundant features. As a result, some essential information is discarded. Motivated by this, this paper proposes a Decision- Risk-incorporated OSFS (DRO) algorithm. Its main idea is two-fold: 1) the missing data of sparse streaming features are pre-estimated by using the Latent Factor Analysis (LFA), and 2) the decision risk of relevance and redundancy analysis is minimized on the estimated complete streaming features via the three-way decision (3WD). Extensive empirical studies are conducted on eight real-world datasets. The results show that DRO significantly outperforms five state-of-the-art competitors.