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

Abstract

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 OLIDSPLM. OLIDSPLM 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 OLIDSPLM 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.