  The domain of machine learning is confronted with a crucial research area
known as class imbalance learning, which presents considerable hurdles in
precise classification of minority classes. This issue can result in biased
models where the majority class takes precedence in the training process,
leading to the underrepresentation of the minority class. The random vector
functional link (RVFL) network is a widely used and effective learning model
for classification due to its good generalization performance and efficiency.
However, it suffers when dealing with imbalanced datasets. To overcome this
limitation, we propose a novel graph embedded intuitionistic fuzzy RVFL for
class imbalance learning (GE-IFRVFL-CIL) model incorporating a weighting
mechanism to handle imbalanced datasets. The proposed GE-IFRVFL-CIL model
offers plethora of benefits: $(i)$ leveraging graph embedding to preserve the
inherent topological structure of the datasets, $(ii)$ employing intuitionistic
fuzzy theory to handle uncertainty and imprecision in the data, $(iii)$ and the
most important, it tackles class imbalance learning. The amalgamation of a
weighting scheme, graph embedding, and intuitionistic fuzzy sets leads to the
superior performance of the proposed models on KEEL benchmark imbalanced
datasets with and without Gaussian noise. Furthermore, we implemented the
proposed GE-IFRVFL-CIL on the ADNI dataset and achieved promising results,
demonstrating the model's effectiveness in real-world applications. The
proposed GE-IFRVFL-CIL model offers a promising solution to address the class
imbalance issue, mitigates the detrimental effect of noise and outliers, and
preserves the inherent geometrical structures of the dataset.
