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A self-training hierarchical prototype-based approach for semi-supervised classification

Gu, Xiaowei (2020) A self-training hierarchical prototype-based approach for semi-supervised classification. Information Sciences, 535 . pp. 204-224. ISSN 0020-0255. (doi:10.1016/j.ins.2020.05.018) (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:90181)

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.2020.05.018

Abstract

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.

Item Type: Article
DOI/Identification number: 10.1016/j.ins.2020.05.018
Uncontrolled keywords: Self-training; Prototype-based; Hierarchical structure; Semi-supervised learning; Classification
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: 13 Sep 2021 11:53 UTC
Last Modified: 14 Sep 2021 08:50 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90181 (The current URI for this page, for reference purposes)
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