Home > Research > Publications & Outputs > Semi-supervised deep rule-based approach for im...

Electronic data

  • SemisupervisedDRB_revision_v1

    Rights statement: 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

    Accepted author manuscript, 1 MB, PDF document

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Links

Text available via DOI:

View graph of relations

Semi-supervised deep rule-based approach for image classification

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>07/2018
<mark>Journal</mark>Applied Soft Computing
Volume68
Number of pages16
Pages (from-to)53-68
Publication statusPublished
Early online date30/03/18
Original languageEnglish

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.

Bibliographic note

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