MOELIGA用进化算法找更少且更准的特征组合。
MOELIGA: a multi-objective evolutionary approach for feature selection with local improvement
- 用多目标遗传算法加局部优化，自动搜寻最优特征子集。
- 在14个数据集上比11种主流方法更小的特征集达到相当或更好准确率。
- 适合高维数据中兼顾精度与特征数量的场景，如生物信息学。
特征选择是实际机器学习中的关键问题。由于穷举搜索对中等数量特征已不可行，需采用智能搜索策略以找到最优子集，同时考虑特征间相互作用对类别可分性的影响。平衡特征子集大小与分类准确率构成多目标优化挑战。本文提出MOELIGA，一种融合进化局部改进策略的多目标遗传算法，通过演化从属种群来优化特征子集。该方法采用基于拥挤度的适应度共享机制和Sigmoid变换以增强多样性并引导紧凑性，同时引入基于几何的客观指标促进分类器独立性。在14个不同数据集上的实验表明，相较于11种现有先进方法，MOELIGA能识别出更小的特征子集，并保持优异或相当的分类性能。这些结果表明，MOELIGA有效解决了准确率与维度之间的权衡，在复杂高维场景中提供了稳健且可扩展的多目标特征选择方案。
原文摘要 · Abstract (English)
Selecting the most relevant or informative features is a key issue in actual machine learning problems. Since an exhaustive search is not feasible even for a moderate number of features, an intelligent search strategy must be employed for finding an optimal subset, which implies considering how features interact with each other in promoting class separability. Balancing feature subset size and classification accuracy constitutes a multi-objective optimization challenge. Here we propose MOELIGA, a multi-objective genetic algorithm incorporating an evolutionary local improvement strategy that evolves subordinate populations to refine feature subsets. MOELIGA employs a crowding-based fitness sharing mechanism and a sigmoid transformation to enhance diversity and guide compactness, alongside a geometry-based objective promoting classifier independence. Experimental evaluation on 14 diverse datasets demonstrates MOELIGA's ability to identify smaller feature subsets with superior or comparable classification performance relative to 11 state-of-the-art methods. These findings suggest MOELIGA effectively addresses the accuracy-dimensionality trade-off, offering a robust and adaptable approach for multi-objective feature selection in complex, high-dimensional scenarios.
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