用粒子群优化提升不确定数据下的在线特征选择精度
Particle swarm optimization for online sparse streaming feature selection under uncertainty
- 引入粒子群算法降低特征与标签间不确定性
- 三重决策理论处理监督学习中的特征模糊性
- 在六个真实数据集上表现优于传统方法
在高维流式数据的实际应用中，在线流式特征选择（OSFS）被广泛采用。然而，由于传感器故障或技术限制，实际部署常面临数据不完整问题。尽管基于潜在因子分析的在线稀疏流式特征选择（OS2FS）缓解了这一问题，现有方法仍难以应对特征-标签关系的不确定性，导致模型僵化、性能下降。为此，本文提出一种受粒子群优化（PSO）增强的不确定性感知在线稀疏流式特征选择框架（POS2FS）。该方法引入：1）基于PSO的监督机制以降低特征-标签关系的不确定性；2）三重决策理论以管理监督学习中的特征模糊性。在六个真实数据集上的严格测试表明，POS2FS显著优于传统OSFS和OS2FS方法，在更鲁棒的特征子集选择下实现更高准确率。
原文摘要 · Abstract (English)
In real-world applications involving high-dimensional streaming data, online streaming feature selection (OSFS) is widely adopted. Yet, practical deployments frequently face data incompleteness due to sensor failures or technical constraints. While online sparse streaming feature selection (OS2FS) mitigates this issue via latent factor analysis-based imputation, existing methods struggle with uncertain feature-label correlations, leading to inflexible models and degraded performance. To address these gaps, this work proposes POS2FS-an uncertainty-aware online sparse streaming feature selection framework enhanced by particle swarm optimization (PSO). The approach introduces: 1) PSO-driven supervision to reduce uncertainty in feature-label relationships; 2) Three-way decision theory to manage feature fuzziness in supervised learning. Rigorous testing on six real-world datasets confirms POS2FS outperforms conventional OSFS and OS2FS techniques, delivering higher accuracy through more robust feature subset selection.
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