Multilayer Stacked Evolving Fuzzy System Combined With Compressed Representation Learning
Hui Huang, Hai-Jun Rong, Zhao-Xu Yang, Chi‐Man Vong · IEEE Transactions on Fuzzy Systems · 2023
In order to process the high-dimensionally complicated problems, the intelligence systems need to go deeper to learn high-level data representation. In this article, based on the stacked generalization principle, a multilayered stacked learning system is proposed. Like the deep networks, the proposed system is organized in a layer-by-layer way with evolving fuzzy systems (EFSs) as its base-building units. Each EFS is designed as an autoencoder (AE) to learn the simpler data representation, then multiple EFS-based AEs are stacked in a feedforward manner for learning more complex data representation. Since there exists the redundant or irrelevant information in the new data representation, which may limit high generalization, a novel feature compressing layer is followed by each EFS-based AE to refine the new data representation and reduce the feature dimension via very sparse random projection (VSRP). The proposed system is featured in the following merits: 1) more expressive and complex data representation can be learned in a stacked multilayer architecture; 2) manually tuning the structure of each AE is avoided in every layer since the EFS can self-adapt both its structure and parameters online; 3) the resulting system can avoid overfitting and obtain higher generalization by removing redundant or irrelevant information via VSRP; 4) the steady-state error analysis of the proposed system is studied based on the separable approximation property, which guarantees the learning convergence of the resulting system. Our experimental results on various benchmark datasets indicate the efficacy of the proposed system.