Particle swarm optimization for online sparse streaming feature selection under uncertainty

TL;DR

This paper introduces POS2FS, an uncertainty-aware online feature selection framework using particle swarm optimization, which improves robustness and accuracy in high-dimensional streaming data with uncertain feature-label relationships.

Contribution

It presents a novel PSO-based method with three-way decision theory for online sparse feature selection under uncertainty, addressing limitations of existing approaches.

Findings

POS2FS outperforms traditional OSFS methods in accuracy.

The framework effectively manages feature-label uncertainty.

Experimental results on six datasets demonstrate robustness.

Abstract

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.