Semi-supervised deep rule-based approach for image classification

Xiaowei Gu, Plamen Parvanov Angelov

Research output: Contribution to journalArticlepeer-review

37 Citations (SciVal)
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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.
Original languageEnglish
Pages (from-to)53-68
Number of pages16
JournalApplied Soft Computing
Volume68
Early online date30 Mar 2018
DOIs
Publication statusPublished - 30 Jul 2018
Externally publishedYes

Keywords

  • Deep rule-based (DRB) classifier
  • Fuzzy rules
  • Prototype-based models
  • Self-organising classifier
  • Semi-supervised learning
  • Transparency and interpretability

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