🤖 AI Summary
This study addresses the challenge of load prediction in multi-port scatterers, where high dimensionality and the complex nonlinear relationship between impedance and scattering responses hinder accurate modeling. To overcome this, the authors propose a two-stage clustering-regression framework: first clustering S-parameter data and then performing regression within each cluster. The work introduces the novel “Reality-Unified Index” (RUI) to holistically evaluate model performance under conflicting multi-objective criteria and systematically optimizes the combination of clustering and regression techniques. Experimental results demonstrate that the proposed architecture reduces RMSE by 46% when implemented with gradient boosting models. Furthermore, RUI-based validation identifies K-means clustering paired with k-nearest neighbors regression as the optimal configuration, significantly enhancing both prediction accuracy and generalization capability.
📝 Abstract
Predicting interdependent load values in multiport scatterers is challenging due to high dimensionality and complex dependence between impedance and scattering ability, yet this prediction remains crucial for the design of communication and measurement systems. In this paper, we propose a two-stage cluster-then-predict framework for multiple load values prediction task in multiport scatterers. The proposed cluster-then-predict approach effectively captures the underlying functional relation between S-parameters and corresponding load impedances, achieving up to a 46% reduction in Root Mean Square Error (RMSE) compared to the baseline when applied to gradient boosting (GB). This improvement is consistent across various clustering and regression methods. Furthermore, we introduce the Real-world Unified Index (RUI), a metric for quantitative analysis of trade-offs among multiple metrics with conflicting objectives and different scales, suitable for performance assessment in realistic scenarios. Based on RUI, the combination of K-means clustering and k-nearest neighbors (KNN) is identified as the optimal setup for the analyzed multiport scatterer.