Hierarchical Fuzzy Topological System for High-Dimensional Data Regression Problems

High-dimensional data regression presents significant challenges due to factors such as strong nonlinearity among features, an excessive number of intermediate variables, and rule explosion, all of which hinder the model's ability to capture complex features and achieve low regression accuracy. This article proposes a method for high-dimensional data regression using a hierarchical fuzzy topological system (HFTS). The HFTS adopts a modular design, where each layer consists of an independent fuzzy logic system, enabling flexible operation based on feature distribution and output requirements. It utilizes a graph neural network based hierarchical feature classification approach to group high-dimensional data,…