Abstract
•A novel semi-supervised deep rule-based (SSDRB) classifier with a prototype-based nature is introduced.•The semi-supervised learning process of the SSDRB classifier is self-organising and highly transparent.•The SSDRB classifier is able to generate human interpretable IF...THEN... rules.•The SSDRB classifier is able to perform classification on out-of-sample images.•The SSDRB classifier outperforms the state-of-art approaches in classification accuracy.
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.