Online Sparse Streaming Feature Selection Using Adapted Classification
This work addresses online feature selection for streaming data, offering an incremental improvement by handling missing data and refining feature relevance classification.
The paper tackled the problem of online streaming feature selection by proposing OS2FS-AC, which uses Latent Factor Analysis to estimate missing data and an adaptive method to classify features into strongly relevant, weakly relevant, and irrelevant categories, resulting in better performance than state-of-the-art algorithms on ten real-world datasets.
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.