Intuitionistic Fuzzy Stochastic Configuration Networks for Solving Binary Classification Problems

Stochastic configuration network (SCN) is an emerging type of random neural network that allocates node parameters via supervised mechanisms to ensure universal approximation capability. However, outliers and noise in realworld data can adversely affect SCN's classification performance. To enhance SCN for binary classification, we introduce intuitionistic fuzzy set concepts to propose intuitionistic fuzzy SCN (IFSCN). Unlike SCN, IFSCN allocates an intuitionistic fuzzy number to every sample by computing degrees of membership and non-membership, thereby crafting an optimal classifier through strategic weighting of samples to mitigate the influence of noise. Furthermore, stochastic configuration sparse autoencoders (SCSAE) effectively learn sparse features using L1 regularization. By integrating multiple SC-SAE models, we extract robust sparse feature representations. We then propose hierarchical IFSCN (IFHSCN) built on SC-SAE for enhanced accuracy. Comprehensive experiments on 8 benchmark datasets demonstrate IFSCN and IFHSCN achieve superior binary classification performance over state-of-the-art models like intuitionistic fuzzy twin SVM, kernel ridge regression, random vector functional link networks. Overall, this study successfully equips SCN for real-world noisy data via intuitionistic fuzzy sets and sparsity, providing an effective and scalable solution for robust classification.