Online Learning for Fickle Data Streams Without Labels

Online learning for data streams has gained significant attention in recent years. Existing methods impose various limitations on the feature space, classes, and labels of data streams. In contrast, real-world data streams often exhibit dynamic feature spaces, missing or partial feature values, undefined classes, and inaccessible labels. These complexities limit the applicability of current online learning methods to real-world scenarios. To fill the gap, this study explores a new online learning problem and proposes a novel algorithm:OnlineLearning forFickle Data Streams withoutLabels (OLFL). Specifically, OLFL has a triple main idea: 1) It tackles dimensional turbulence through a completion strategy that collaborates with weight vectors, reducing the over-reliance on surviving features; 2) It implements known class classification and new class detection via the density of core points within the neighborhood of each instance; 3) It utilizes a constraint-based adaptive coefficient vector to update model. We evaluate OLFL on extensive real-world and synthetic datasets against strong baselines, covering fickle feature spaces, emerging classes, and varying missingness rates. We further conduct ablation studies and case analyses. The results consistently show that OLFL outperforms the baselines in both accuracy and efficiency.