Semisupervised Fuzzily Weighted Adaptive Boosting for Classification

Fuzzy systems offer a formal and practically popular methodology for modeling nonlinear problems with inherent uncertainties, entailing strong performance and model interpretability. Particularly, semisupervised boosting is widely recognized as a powerful approach for creating stronger ensemble classification models in the absence of sufficient labeled data without introducing any modification to the employed base classifiers. However, the potential of fuzzy systems in semisupervised boosting has not been systematically explored yet. In this study, a novel semisupervised boosting algorithm devised for zero-order evolving fuzzy systems is proposed. It ensures both the consistence among predictions made by individual base classifiers at successive boosting iterations and the respective levels of confidence toward 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 generalize, 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 semisupervised boosting algorithm for constructing ensemble fuzzy classifiers with high accuracy.