Semi-supervised deep rule-based approach for image classification

Gu, Xiaowei and Angelov, Plamen Parvanov (2018) Semi-supervised deep rule-based approach for image classification. Applied Soft Computing, 68. pp. 53-68. ISSN 1568-4946

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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:
Journal Article
Journal or Publication Title:
Applied Soft Computing
Additional Information:
This is the author’s version of a work that was accepted for publication in Applied Soft Computing. 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 Applied Soft Computing, 68, 2018 DOI: 10.1016/j.asoc.2018.03.032
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Deposited On:
21 Mar 2018 10:00
Last Modified:
06 Jan 2023 01:46