A New Predictive Algorithm for Enhancing Regression Performance Using Insights from Random Vine Anchorage Mechanisms
Keywords:
Biologically Inspired Computing, Regression Performance, Random Vine Anchorage MechanismsAbstract
This study proposes a novel regression algorithm termed the Random Vine Anchorage Mechanism (RVAM), inspired by adaptive anchoring behaviors observed in climbing vines. Unlike conventional regression approaches that rely on fixed feature weighting or global optimization objectives, RVAM introduces an anchorage-based learning strategy that dynamically reinforces informative features while attenuating redundant or weakly relevant ones through a vine-structured dependency model.The primary objective of this research is to enhance regression performance under challenging data conditions, including high dimensionality, feature correlation, and noise perturbation. To achieve this, RVAM integrates three key mechanisms: adaptive feature anchorage initialization, random vine-based dependency modeling, and normalized anchorage weight updating. These components jointly enable the proposed algorithm to capture complex feature interactions while maintaining robustness and numerical stability.The effectiveness of RVAM is evaluated using multiple benchmark datasets specifically designed for regression analysis. Experimental results demonstrate that the proposed method consistently outperforms conventional regression models, including linear regression, LASSO, Elastic Net, Random Forest, and Gradient Boosting, across multiple evaluation metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R²), and Mean Absolute Percentage Error (MAPE). On average, RVAM achieves performance improvements ranging from 8% to 15% compared with baseline methods, particularly in datasets characterized by multicollinearity and complex feature dependencies. These findings highlight the potential of biologically inspired anchorage mechanisms as an effective and generalizable strategy for improving regression performance in complex engineering and data-driven applications.
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