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Applications of Semi-supervised Deep Rule-Based Classifiers

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter (peer-reviewed)peer-review

Publication date2019
Host publicationEmpirical Approach to Machine Learning
EditorsPlamen Angelov, Xiaowei Gu
Number of pages20
ISBN (print)9783030023836
<mark>Original language</mark>English

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X


In this chapter, the algorithm summary of the main procedure of the semi-supervised deep rule-based (SS_DRB) classifier described in Chap. 9 is provided, which serves as a powerful extension of the DRB classifier. The offline learning process of the SS_DRB classifier is illustrated and the performance of the SS_DRB algorithm is evaluated based on benchmark image sets. Numerical examples and comparison with the state-of-the-art semi-supervised learning approaches demonstrate that SS_DRB classifier can achieve highly accurate classification results with only a handful of labelled training images, and it consistently outperforms the alternative approaches. The pseudo-code of the main procedure of the SS_DRB classifier and the MATLAB implementations can be found in appendices B.6 and C.6, respectively. © 2019, Springer Nature Switzerland AG.