A Hierarchical Prototype-Based Approach for Classification

Gu, Xiaowei and Ding, Weiping (2019) A Hierarchical Prototype-Based Approach for Classification. Information Sciences, 505. pp. 325-351. ISSN 0020-0255

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In this paper, a novel hierarchical prototype-based (HP) approach for classification is proposed. This approach is able to perceive the data space and derive the multimodal distributions from streaming data at different levels of granularity in an online manner, based on which it further identifies meaningful prototypes to self-organize and self-evolve its hierarchical structure for classification. Thanks to the prototype-based nature, the system structure of the HP classifier is highly transparent, and its learning process is of “one pass” type and computationally lean. Its decision-making process follows the “nearest prototype” principle and is fully explainable. The proposed HP approach is capable of presenting the learned knowledge in an easy-to-interpret prototype-based hierarchical form to users, and is an attractive tool for solving large-scale, complex real-world problems. Numerical examples based on various benchmark problems justify the validity and effectiveness of the proposed concept and general principles.

Item Type: Journal Article
Journal or Publication Title: Information Sciences
Additional Information: This is the author’s version of a work that was accepted for publication in Information Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences, 505, 2019 DOI: 10.1016/j.ins.2019.07.077
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/1700/1706
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 135796
Deposited By: ep_importer_pure
Deposited On: 25 Jul 2019 09:00
Refereed?: Yes
Published?: Published
Last Modified: 23 Feb 2020 05:06
URI: https://eprints.lancs.ac.uk/id/eprint/135796

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