Particle swarm optimization for online sparse streaming feature selection under uncertainty
This work addresses improved feature selection for high-dimensional streaming data in real-world applications like sensor networks, though it appears incremental as it builds on existing OS2FS methods.
The paper tackled the problem of uncertain feature-label correlations in online sparse streaming feature selection under data incompleteness, proposing a framework enhanced by particle swarm optimization and three-way decision theory, which outperformed existing methods on six real-world datasets with higher accuracy.
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.