Semi-Supervised Fuzzily Weighted Adaptive Boosting for Classification

Xiaowei Gu, Plamen Angelov, Qiang Shen

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Fuzzy systems offer a formal and practically popular methodology for modelling nonlinear problems with inherent uncertainties, entailing strong performance and model interpretability. Particularly, semi-supervised boosting is widely recognised as a powerful approach for creating stronger ensemble classification models in the absence of sufficient labelled data without introducing any modification to the employed base classifiers. However, the potential of fuzzy systems in semi-supervised boosting has not been systematically explored yet. In this study, a novel semi-supervised boosting algorithm devised for zero-order evolving fuzzy systems is proposed. It ensures both the consistence amongst predictions made by individual base classifiers at successive boosting iterations and the respective levels of confidence towards their predictions throughout the process of sample weight updating and ensemble output generation. In so doing, the base classifiers are empowered to gradually focus more on challenging samples that are otherwise hard to generalise, enabling the development of more precise integrated classification boundaries. Numerical evaluations on a range of benchmark problems are carried out, demonstrating the efficacy of the proposed semi-supervised boosting algorithm for constructing ensemble fuzzy classifiers with high accuracy.

Original languageEnglish
Number of pages13
JournalIEEE Transactions on Fuzzy Systems
Early online date04 Jan 2024
Publication statusE-pub ahead of print - 04 Jan 2024


  • Boosting
  • classification
  • Classification algorithms
  • Data models
  • fuzzy system
  • Fuzzy systems
  • Predictive models
  • semi-supervised
  • Semisupervised learning
  • Training


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