Online Sparse Feature Selection in Data Streams via Differential Evolution
This work addresses feature selection challenges in streaming data for applications like sensor networks, though it is incremental as it builds on existing OS2FS approaches.
The paper tackles the problem of online sparse feature selection in high-dimensional streaming data with missing values by introducing ODESFS, which uses latent factor analysis for imputation and differential evolution for feature evaluation, achieving superior accuracy over state-of-the-art methods on six real-world datasets.
The processing of high-dimensional streaming data commonly utilizes online streaming feature selection (OSFS) techniques. However, practical implementations often face challenges with data incompleteness due to equipment failures and technical constraints. Online Sparse Streaming Feature Selection (OS2FS) tackles this issue through latent factor analysis-based missing data imputation. Despite this advancement, existing OS2FS approaches exhibit substantial limitations in feature evaluation, resulting in performance deterioration. To address these shortcomings, this paper introduces a novel Online Differential Evolution for Sparse Feature Selection (ODESFS) in data streams, incorporating two key innovations: (1) missing value imputation using a latent factor analysis model, and (2) feature importance evaluation through differential evolution. Comprehensive experiments conducted on six real-world datasets demonstrate that ODESFS consistently outperforms state-of-the-art OSFS and OS2FS methods by selecting optimal feature subsets and achieving superior accuracy.