arXiv:2511.06454math.OCcs.LG2025-11被引 1
用演化模拟自动分配特征权重，提升多目标数据分析效果
Feature weighting for data analysis via evolutionary simulation
- 通过复制子动态在单纯形上演化特征权重
- 权重序列全局收敛至唯一非退化解
- 适合需要自动特征重要性评估的数据分析场景
我们研究了一种在离散多目标数据问题中，于标量化前分配权重的算法。该算法基于标准单纯形上的复制子型动态演化权重（可解释为特征相关性），更新指标由归一化数据矩阵计算得出。我们证明了所得权重序列全局收敛至唯一内部平衡点，从而获得非退化的极限权重。
原文摘要 · Abstract (English)
We analyze an algorithm for assigning weights prior to scalarization in discrete multi-objective problems arising from data analysis. The algorithm evolves weights (interpreted as the relevance of features) by a replicator-type dynamic on the standard simplex, with update indices computed from a normalized data matrix. We prove that the resulting sequence converges globally to a unique interior equilibrium, yielding non-degenerate limiting weights.
特征权重多目标优化演化算法
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