Online incremental hierarchical classification resonance network

Abstract

Hierarchical classification is imperative in that almost all objects are described in hierarchical semantics. If a classification method enables incremental class learning to learn new objects online, it will be practically used for real-time applications. In this sense, we propose online incremental hierarchical classification resonance network (OIHCRN) that enables online incremental class learning in hierarchical classification. OIHCRN has a structure that grows horizontally and vertically online according to object classes, so that a newly added object can be classified. By the proposed process of scale-preserving projection and prior label appending, OIHCRN reflects the class dependency between class levels and simultaneously normalizes the input vector online. Additionally, to reduce the model complexity and improve performance, two auxiliary strategies, named OIHCRN with class END and OIHCRN with differentiated class labels, are introduced. To demonstrate the effectiveness of OIHCRNs, experiments are carried out for benchmark datasets and then for a multimedia recommendation system.