符号回归中进化特征构建的自适应保护机制及其在信用分类中的应用
Adaptive Protection for Evolutionary Feature Construction in Symbolic Regression with Application to Credit Classification
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- Centre for Data Science and Artificial Intelligence（数据科学与人工智能中心）
- School of Engineering and Computer Science（工程与计算机科学学院）
- Victoria University of Wellington（惠灵顿维多利亚大学）
- Business School, Sichuan University（四川大学商学院）
- Michigan State University（密歇根州立大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
针对符号回归进化特征构建中重要特征易丢失的问题，提出基于特征重要性的自适应保护机制，经98个回归基准数据集和2个信用分类数据集验证，可提升解质量与搜索效率。
中文摘要 AI 辅助
进化特征构建通过自动发现输入特征的有用变换来提升简单基学习器，在符号回归中展现出强大潜力。然而现有方法常缺乏明确机制以保留进化过程中发现的重要构建特征，当遗传算子破坏有效特征时，有价值的遗传物质会丢失。本文提出一种自适应保护机制，利用特征重要性指标在进化过程中有选择地保留构建特征；该机制为更重要的构建特征提供更强保护，同时允许较不重要的特征被修改，并融入更重要特征的有用构建块。我们采用多种特征重要性计算方法评估该方法，验证其在不同基学习器上的鲁棒性；在98个回归基准数据集上的实验表明，所提机制相比基线方法可持续提升解的质量，在两个信用分类数据集上的实验则证明该方法能有效扩展至提升符号回归之外的搜索效率。
英文摘要
Evolutionary feature construction has shown strong promise in symbolic regression by automatically discovering informative transformations of input features that enhance a simple base learner. However, existing approaches often lack explicit mechanisms to preserve important constructed features discovered during evolution, and valuable genetic material can be lost when genetic operators disrupt effective features. This paper introduces an adaptive protection mechanism that leverages feature importance metrics to selectively preserve constructed features during evolution. The mechanism provides stronger protection for more important constructed features while still allowing less important features to be modified and to incorporate useful building blocks from more important features. We evaluate the approach using multiple feature importance calculation methods and demonstrate its robustness across different base learners. Experimental results on 98 regression benchmark datasets show that the proposed mechanism consistently improves solution quality over baseline approaches, and experiments on two credit classification datasets demonstrate that the method also extends effectively to improve search effectiveness beyond symbolic regression.
