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Self-organizing Divisive Hierarchical Voronoi Tessellation-based classifier

Gu, Xiaowei, Shen, Qiang (2022) Self-organizing Divisive Hierarchical Voronoi Tessellation-based classifier. Information Sciences, 603 . pp. 106-139. ISSN 0020-0255. (doi:10.1016/j.ins.2022.04.049) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:94753)

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https://doi.org/10.1016/j.ins.2022.04.049

Abstract

In this paper, a novel approach to the self-organization of hierarchical prototype-based classifiers from data is proposed. The approach recursively partitions the data at multiple levels of granularity into shape-free clusters of different sizes, resembling Voronoi tessellation, and naturally aggregates the resulting cluster medoids into a multi-layered prototype-based structure according to their descriptive abilities. Different from conventional classification models, it is nonparametric and entirely data-driven, and the learned model can offer a high-level of transparency and interpretability thanks to the underlying prototype-based nature. The system identification process underpinning the approach is driven by the aim of separating data samples of different classes into nonoverlapping multi-granular clusters. Its associated decision-making process follows the “nearest prototype” principle and hence, the rationales of the subsequent decisions made can be explicitly explained. Experimental studies based on popular benchmark classification problems, as well as on a practical application to remote sensing image classification, demonstrate the efficacy of the proposed approach.

Item Type: Article
DOI/Identification number: 10.1016/j.ins.2022.04.049
Uncontrolled keywords: classification; divisive partitioning; prototype; self-organizing; hierarchical model
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Xiaowei Gu
Date Deposited: 25 Apr 2022 22:22 UTC
Last Modified: 26 May 2022 14:18 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/94753 (The current URI for this page, for reference purposes)
Gu, Xiaowei: https://orcid.org/0000-0001-9116-4761
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