Online Sparse Streaming Feature Selection Using Adapted Classification

TL;DR

This paper introduces OS2FS-AC, an online sparse streaming feature selection method that completes missing data and adaptively classifies features, outperforming existing algorithms on real-world datasets.

Contribution

The paper proposes a novel online feature selection approach that handles missing data and adaptively classifies feature relevance, improving performance over state-of-the-art methods.

Findings

OS2FS-AC outperforms existing algorithms on ten real-world datasets.

The method effectively completes missing data using Latent Factor Analysis.

Adaptive thresholding improves feature relevance classification.

Abstract

Traditional feature selections need to know the feature space before learning, and online streaming feature selection (OSFS) is proposed to process streaming features on the fly. Existing methods divide features into relevance or irrelevance without missing data, and deleting irrelevant features may lead to in-formation loss. Motivated by this, we focus on completing the streaming feature matrix and division of feature correlation and propose online sparse streaming feature selection based on adapted classification (OS2FS-AC). This study uses Latent Factor Analysis (LFA) to pre-estimate missed data. Besides, we use the adaptive method to obtain the threshold, divide the features into strongly relevant, weakly relevant, and irrelevant features, and then divide weak relevance with more information. Experimental results on ten real-world data sets demonstrate that OS2FS-AC performs better than state-of-the-art algo-rithms.