Large-scale data classification based on the integrated fusion of fuzzy learning and graph neural network

Deep learning and fuzzy models provide powerful and practical techniques for solving large-scale deep-learning tasks. The fusion technique on deep learning and fuzzy system are generally classified into ensemble and integrated modes and materializes in information fusion, model fusion, and feature fusion. In an ensemble-based fusion, the fuzzy model either acts as an activation function or is operated as a separate process aggregating/preprocessing the information. Some early attempts in the field have successfully fused deep neural networks and fuzzy modeling concepts in ensemble mode. However, no effective attempts were made to fuse fuzzy models as an integrated feature-level fusion learning with graph neural networks (GNNs). This is mainly due to two challenges related to this fusion: (1) the number of fuzzy rules grows exponentially with the number of features that causes computational inefficiency, and (2) the solution space created by this fusion of fuzzy rules becomes complex due to multiple regression relations between inputs and outputs. Additionally, a simple linear regression at the output space would not be sufficient to model deep learning tasks. Therefore, this paper addresses these challenges by proposing a feature-level fusion method to fuse deep learning and fuzzy modeling where the latter technique is for integrated feature learning, called fuzzy forest graph neural network (FuzzyGNN), which creates a fuzzy learning forest fusing the linear graph transformers for deep learning tasks. We conducted experiments on fourteen machine learning datasets to test and validate the efficiency of the proposed FuzzyGNN model. Compared to state-of-the-art methods, our algorithm achieves the best results on four out of five machine learning datasets. The source code will be available at https://github.com/lingping-fuzzy/ and https://github.com/P-N-Suganthan.