通过重要性自适应保护，防止进化特征构造中关键特征丢失。
Adaptive Protection for Evolutionary Feature Construction in Symbolic Regression with Application to Credit Classification
- 根据特征重要性动态保护关键构造特征
- 98个基准数据集上显著提升回归性能
- 适用于信用分类等实际场景，拓展性强
进化特征构造在符号回归中展现出巨大潜力，能自动发现增强基础学习器的输入特征变换。但现有方法缺乏显式机制来保留进化过程中发现的重要构造特征，遗传算子可能破坏有效特征导致宝贵遗传信息丢失。本文提出一种自适应保护机制，利用特征重要性度量选择性地保护构造特征：重要特征获得更强保护，次要特征仍可调整并吸收重要特征中的有用构建块。通过多种重要性计算方法评估，该机制在不同基础学习器下均表现稳健。在98个回归基准数据集上的实验表明，该方法持续优于基线；在两个信用分类数据集上的实验也证明其在符号回归之外仍能有效提升搜索效率。
原文摘要 · Abstract (English)
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.
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