期刊
INFORMATION SCIENCES
卷 535, 期 -, 页码 204-224出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2020.05.018
关键词
Self-training; Prototype-based; Hierarchical structure; Semi-supervised learning; Classification
This paper introduces a novel self-training hierarchical prototype-based approach for semi-supervised classification. The proposed approach firstly identifies meaningful prototypes from labelled samples at multiple levels of granularity and, then, self-organizes a highly transparent, multi-layered recognition model by arranging them in a form of pyramidal hierarchies. After this, the learning model continues to self-evolve its structure and self-expand its knowledge base to incorporate new patterns recognized from unlabelled samples by exploiting the pseudo-label technique. Thanks to its prototype-based nature, the overall computational process of the proposed approach is highly explainable and traceable. Experimental studies with various benchmark image recognition problems demonstrate the state-of-the-art performance of the proposed approach, showing its strong capability to mine key information from unlabelled data for classification. (C) 2020 Elsevier Inc. All rights reserved.
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