  The ensemble deep random vector functional link (edRVFL) neural network has
demonstrated the ability to address the limitations of conventional artificial
neural networks. However, since edRVFL generates features for its hidden layers
through random projection, it can potentially lose intricate features or fail
to capture certain non-linear features in its base models (hidden layers). To
enhance the feature learning capabilities of edRVFL, we propose a novel edRVFL
based on fuzzy inference system (edRVFL-FIS). The proposed edRVFL-FIS leverages
the capabilities of two emerging domains, namely deep learning and ensemble
approaches, with the intrinsic IF-THEN properties of fuzzy inference system
(FIS) and produces rich feature representation to train the ensemble model.
Each base model of the proposed edRVFL-FIS encompasses two key feature
augmentation components: a) unsupervised fuzzy layer features and b) supervised
defuzzified features. The edRVFL-FIS model incorporates diverse clustering
methods (R-means, K-means, Fuzzy C-means) to establish fuzzy layer rules,
resulting in three model variations (edRVFL-FIS-R, edRVFL-FIS-K, edRVFL-FIS-C)
with distinct fuzzified features and defuzzified features. Within the framework
of edRVFL-FIS, each base model utilizes the original, hidden layer and
defuzzified features to make predictions. Experimental results, statistical
tests, discussions and analyses conducted across UCI and NDC datasets
consistently demonstrate the superior performance of all variations of the
proposed edRVFL-FIS model over baseline models. The source codes of the
proposed models are available at https://github.com/mtanveer1/edRVFL-FIS.
