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A multi-granularity locally optimal prototype-based approach for classification

Gu, Xiaowei, Li, Miqing (2021) A multi-granularity locally optimal prototype-based approach for classification. Information Sciences, 569 . pp. 157-183. ISSN 0020-0255. (doi:10.1016/j.ins.2021.04.039) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90394)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1016/j.ins.2021.04.039

Abstract

Prototype-based approaches generally provide better explainability and are widely used for classification. However, the majority of them suffer from system obesity and lack transparency on complex problems. In this paper, a novel classification approach with a multi-layered system structure self-organized from data is proposed. This approach is able to identify local peaks of multi-modal density derived from static data and filter out more representative ones at multiple levels of granularity acting as prototypes. These prototypes are then optimized to their locally optimal positions in the data space and arranged in layers with meaningful dense links in-between to form pyramidal hierarchies based on the respective levels of granularity accordingly. After being primed offline, the constructed classification model is capable of self-developing continuously from streaming data to self-expend its knowledge base. The proposed approach offers higher transparency and is convenient for visualization thanks to the hierarchical nested architecture. Its system identification process is objective, data-driven and free from prior assumptions on data generation model with user- and problem- specific parameters. Its decision-making process follows the “nearest prototype” principle, and is highly explainable and traceable. Numerical examples on a wide range of benchmark problems demonstrate its high performance.

Item Type: Article
DOI/Identification number: 10.1016/j.ins.2021.04.039
Uncontrolled keywords: Local optimality; Multi-granularity; Prototype-based; Pyramidal hierarchy
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 28 Sep 2021 08:49 UTC
Last Modified: 29 Sep 2021 11:36 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90394 (The current URI for this page, for reference purposes)
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