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The domain of machine learning is confronted with a crucial research areaknown as class imbalance learning, which presents considerable hurdles in theprecise classification of minority classes. This issue can result in biasedmodels where the majority class takes precedence in the training process,leading to the underrepresentation of the minority class. The random vectorfunctional link (RVFL) network is a widely-used and effective learning modelfor classification due to its speed and efficiency. However, it suffers fromlow accuracy when dealing with imbalanced datasets. To overcome thislimitation, we propose a novel graph embedded intuitionistic fuzzy RVFL forclass imbalance learning (GE-IFRVFL-CIL) model incorporating a weightingmechanism to handle imbalanced datasets. The proposed GE-IFRVFL-CIL model has aplethora of benefits, such as $(i)$ it leverages graph embedding to extractsemantically rich information from the dataset, $(ii)$ it uses intuitionisticfuzzy sets to handle uncertainty and imprecision in the data, $(iii)$ and themost important, it tackles class imbalance learning. The amalgamation of aweighting scheme, graph embedding, and intuitionistic fuzzy sets leads to thesuperior performance of the proposed model on various benchmark imbalanceddatasets, including UCI and KEEL. Furthermore, we implement the proposedGE-IFRVFL-CIL on the ADNI dataset and achieved promising results, demonstratingthe model's effectiveness in real-world applications. The proposed methodprovides a promising solution for handling class imbalance in machine learningand has the potential to be applied to other classification problems.

Graph Embedded Intuitionistic Fuzzy RVFL for Class Imbalance Learning