Online Learning in Varying Feature Spaces with Informative Variation

Most conventional literature on online learning implicitly assumes a static feature space. However, in real-world applications, the feature space may vary over time due to the emergence of new features and the vanishing of outdated features. This phenomenon is referred to as online learning with Varying Feature Space (VFS). Recently, there has been increasing attention towards exploring this online learning paradigm. However, none of the existing approaches have taken into account the potentially informative information conveyed by the presence or absence (i.e., variation in this paper) of each feature. This indicates that the existence of certain features in the VFS can be correlated with the class labels. If properly utilized for the learning process, such information can potentially enhance predictive performance. To this end, we formally define and present a learning framework to address this specific learning scenario, which we refer to as Online learning in Varying Feature space with Informative Variation (abbreviated as OVFIV). The framework aims to answer two key questions: how to learn a model that captures the association between the existence of features and the class labels, and how to incorporate this information into the prediction process to improve performance. The validity of our proposed method is verified through theoretical analyses and empirical studies conducted on 17 datasets from diverse fields.