Online Sparse Streaming Feature Selection Using Adapted Classification. (arXiv:2302.14056v1 [cs.LG])

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.