Online learning from incomplete data streams with partial labels for multi-classification

Online learning from the data streams is a research hotspot due to adaptive responses to real-time data arrival and fleeting. Existing approaches can only handle real-world scenarios partially due to constraints such as binary classification, complete labels, and fixed feature spaces. Learning from incomplete data streams with partial labels for multi-classification is crucial but rarely investigated due to its complexity and variability. To address this issue, we propose a novel Online Learning approach from Incomplete Data Streams with Partial Labels for Multi-classification, named OLIDS_PLM. OLIDS_PLM includes three main ideas: a) exploiting feature similarity to re-weight the most informative features in incomplete feature space (IFS) to avoid bias caused by filling in missing features, b) using self-train to label unlabeled instances and filter outliers, and c) utilizing the difference in the distribution between instances and model generated to detect concept drifts adaptively. We experimentally evaluated OLIDS_PLM and its rivals in handling the IFS, partial labels, and concept drifts to validate its effectiveness. The code is released at https://github.com/youdianlong/OLIDSPLM.