Online Sparse Feature Selection in Data Streams via Differential Evolution

TL;DR

This paper presents ODESFS, a novel online sparse feature selection method for data streams that effectively handles missing data and improves feature evaluation using differential evolution, leading to better accuracy on real-world datasets.

Contribution

Introduces ODESFS, combining latent factor analysis for missing data imputation with differential evolution for feature importance evaluation in online streaming scenarios.

Findings

Outperforms existing OSFS and OS2FS methods in accuracy

Effectively handles missing data in streaming environments

Selects more relevant feature subsets

Abstract

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.