Skip to main content

Semi-supervised deep rule-based approach for image classification

Gu, Xiaowei, Angelov, Plamen P. (2018) Semi-supervised deep rule-based approach for image classification. Applied Soft Computing, 68 . pp. 53-68. ISSN 1568-4946. (doi:10.1016/j.asoc.2018.03.032) (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:90203)

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.asoc.2018.03.032

Abstract

In this paper, a semi-supervised learning approach based on a deep rule-based (DRB) classifier is introduced. With its unique prototype-based nature, the semi-supervised DRB (SSDRB) classifier is able to generate human interpretable IF...THEN...rules through the semi-supervised learning process in a self-organising and highly transparent manner. It supports online learning on a sample-by-sample basis or on a chunk-by-chunk basis. It is also able to perform classification on out-of-sample images. Moreover, the SSDRB classifier can learn new classes from unlabelled images in an active way becoming dynamically self-evolving. Numerical examples based on large-scale benchmark image sets demonstrate the strong performance of the proposed SSDRB classifier as well as its distinctive features compared with the “state-of-the-art” approaches.

Item Type: Article
DOI/Identification number: 10.1016/j.asoc.2018.03.032
Uncontrolled keywords: Semi-supervised learning; Deep rule-based (DRB) classifier; Prototype-based models; Fuzzy rules; Self-organising classifier; Transparency and interpretability
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: 14 Sep 2021 13:00 UTC
Last Modified: 15 Sep 2021 15:52 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90203 (The current URI for this page, for reference purposes)
  • Depositors only (login required):