基于相似性搜索的符号回归新方法，提升表达式可解释性与准确性。
SPINEX_ Symbolic Regression: Similarity-based Symbolic Regression with Explainable Neighbors Exploration
- 通过相似性匹配寻找高价值数学表达式
- 在180个基准函数上表现优于主流算法
- 适合需要可解释模型的科研与工程场景
本文提出一种基于SPINEX（相似性预测与可解释邻居探索）家族的新符号回归算法——SPINEX_SymbolicRegression。该方法采用相似性驱动策略，识别满足精度与结构相似性指标的高优表达式。我们在涵盖随机生成表达式及真实物理现象的国际基准数据集上，对超过180个数学基准函数进行了广泛测试。评估指标包括准确性、表达式中运算符与变量的相似度、种群规模及收敛代数。结果表明，SPINEX_SymbolicRegression表现稳定，在部分情况下超越领先算法。此外，通过深入实验验证了其可解释性能力。
原文摘要 · Abstract (English)
This article introduces a new symbolic regression algorithm based on the SPINEX (Similarity-based Predictions with Explainable Neighbors Exploration) family. This new algorithm (SPINEX_SymbolicRegression) adopts a similarity-based approach to identifying high-merit expressions that satisfy accuracy- and structural similarity metrics. We conducted extensive benchmarking tests comparing SPINEX_SymbolicRegression to over 180 mathematical benchmarking functions from international problem sets that span randomly generated expressions and those based on real physical phenomena. Then, we evaluated the performance of the proposed algorithm in terms of accuracy, expression similarity in terms of presence operators and variables (as compared to the actual expressions), population size, and number of generations at convergence. The results indicate that SPINEX_SymbolicRegression consistently performs well and can, in some instances, outperform leading algorithms. In addition, the algorithm's explainability capabilities are highlighted through in-depth experiments.
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